------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\micha\OneDrive\Documents\Heaney_Code_2022-04-14.log
  log type:  text
 opened on:  14 Apr 2022, 22:33:51

. 
. * Michael T. Heaney
. * National Protest Events Analysis, 2017-2018
. * April 14, 2022
. 
. * Code Variables
. 
. generate female = 1 if sex == 1
(1,817 missing values generated)

. replace female = 0 if sex == 0
(1,148 real changes made)

. replace female = 0 if sex == 2
(48 real changes made)

. 
. recode inc (0=0) (1=7.5) (2=20) (3=37.5) (4=62.5) (5=87.5) (6=112.5) (7=137.5) (8=250) (9=450), gen(income)
(3051 differences between inc and income)

. 
. generate logdistance = log(distance) if distance >= 1
(771 missing values generated)

. replace logdistance = 0 if distance < 1
(11 real changes made)

. 
. recode ideo (9=1) (8=2) (7=3) (6=4) (5=5) (4=6) (3=7) (2=8) (1=9) (10=.), gen(r_ideo)
(3081 differences between ideo and r_ideo)

. 
. recode pid (1=3) (2=2) (3=1) (4=0) (5=1) (6=2) (7=3) (8=.), gen(pid_folded)
(3426 differences between pid and pid_folded)

. 
. recode aidimp (1=4) (2=3) (3=2) (4=1) (5=.), gen(r_aidimp)
(3401 differences between aidimp and r_aidimp)

. 
. recode know (1=3) (2=2) (3=1), gen(r_know)
(1900 differences between know and r_know)

. 
. recode intersectionality (1=1) (2=2) (3=3) (4=4) (5=5) (6=.) (7=5), gen(r_intersectionality)
(47 differences between intersectionality and r_intersectionality)

. 
. recode demworks (1=4) (2=3) (3=2) (4=1), gen(r_demworks)
(3479 differences between demworks and r_demworks)

. 
. recode ang (1=5) (2=4) (3=3) (4=2) (5=1), gen(r_ang)
(2369 differences between ang and r_ang)

. 
. * Table 1: Event Sample Sizes and BLM Participation
. 
. sum CaseID blm if RenumberedEvent == 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        182    2.02e+09    321.5614   2.02e+09   2.02e+09
         blm |        153    .6078431    .4898347          0          1

. sum CaseID blm if RenumberedEvent == 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        327    2.02e+09    283.0609   2.02e+09   2.02e+09
         blm |        289     .266436    .4428617          0          1

. sum CaseID blm if RenumberedEvent == 3 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        210    2.02e+09    251.6469   2.02e+09   2.02e+09
         blm |        114    .0350877    .1848139          0          1

. sum CaseID blm if RenumberedEvent == 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        139    2.02e+09    201.8915   2.02e+09   2.02e+09
         blm |        124    .2822581    .4519242          0          1

. sum CaseID blm if RenumberedEvent == 5

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        447    2.02e+09    660.5521   2.02e+09   2.02e+09
         blm |        339    .1769912    .3822254          0          1

. sum CaseID blm if RenumberedEvent == 6 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        368    2.02e+09    422.3408   2.02e+09   2.02e+09
         blm |        316    .2563291    .4372981          0          1

. sum CaseID blm if RenumberedEvent == 7

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        181    2.02e+09    179.4338   2.02e+09   2.02e+09
         blm |        161    .3354037    .4736045          0          1

. sum CaseID blm if RenumberedEvent == 8

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        201    2.02e+09    287.4052   2.02e+09   2.02e+09
         blm |        169     .035503    .1855972          0          1

. sum CaseID blm if RenumberedEvent == 9

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        133    2.02e+09    225.8469   2.02e+09   2.02e+09
         blm |        116    .0258621    .1594123          0          1

. sum CaseID blm if RenumberedEvent == 10

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        210    2.02e+09    273.8092   2.02e+09   2.02e+09
         blm |        192    .5989583    .4913908          0          1

. sum CaseID blm if RenumberedEvent == 11

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         80    2.02e+09    79.54255   2.02e+09   2.02e+09
         blm |         54    .0185185    .1360828          0          1

. sum CaseID blm if RenumberedEvent == 12 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        177    2.02e+09    146.1635   2.02e+09   2.02e+09
         blm |        159     .327044    .4706157          0          1

. sum CaseID blm if RenumberedEvent == 13

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        118    2.02e+09     108.678   2.02e+09   2.02e+09
         blm |        108    .2037037    .4046288          0          1

. sum CaseID blm if RenumberedEvent == 14 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         16    2.02e+09    90.32624   2.02e+09   2.02e+09
         blm |         12           0           0          0          0

. sum CaseID blm if RenumberedEvent == 15 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        289    2.02e+09    363.1651   2.02e+09   2.02e+09
         blm |        246    .3373984     .473786          0          1

. sum CaseID blm if RenumberedEvent == 16 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        144    2.02e+09    207.7891   2.02e+09   2.02e+09
         blm |        135    .1851852    .3898945          0          1

. sum CaseID blm if RenumberedEvent == 17

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         90    2.02e+09    189.6666   2.02e+09   2.02e+09
         blm |         79    .1265823    .3346291          0          1

. sum CaseID blm if RenumberedEvent == 18

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |        211    2.02e+09    240.9941   2.02e+09   2.02e+09
         blm |        201    .3880597     .488525          0          1

. sum CaseID blm if RenumberedEvent == 19 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         47    2.02e+09    126.1904   2.02e+09   2.02e+09
         blm |         31    .4193548    .5016103          0          1

. sum CaseID blm if RenumberedEvent == 20

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |          7    2.02e+09    2.160247   2.02e+09   2.02e+09
         blm |          5           0           0          0          0

. sum CaseID blm if RenumberedEvent == 21

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         86    2.02e+09    102.5861   2.02e+09   2.02e+09
         blm |         80        .525    .5025253          0          1

. sum CaseID blm if RenumberedEvent == 22

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         44    2.02e+09    77.42036   2.02e+09   2.02e+09
         blm |         41    .6585366    .4800915          0          1

. sum CaseID blm if RenumberedEvent == 23

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         57    2.02e+09    306.2733   2.02e+09   2.02e+09
         blm |         48    .0208333    .1443376          0          1

. sum CaseID blm if RenumberedEvent == 24

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      CaseID |         96    2.02e+09    231.6423   2.02e+09   2.02e+09
         blm |         87    .6321839    .4850064          0          1

. 
. * Generate Survey Weights for 2017
. 
. generate weight = 1.0

. 
. * Weights for Event 1: Inagural Counterprotests in Washington, DC, 2017
. 
. replace weight  = 0.877075238 if RenumberedEvent==1&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.993208829 if RenumberedEvent==1&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.957894737 if RenumberedEvent==1&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.115993868 if RenumberedEvent==1&sex==.&rwhite==1
(2 real changes made)

. replace weight  = 1.300000000 if RenumberedEvent==1&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.111842105 if RenumberedEvent==1&sex==.&rasian==1
(1 real change made)

. replace weight  = 0.781954887 if RenumberedEvent==1&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.760233918 if RenumberedEvent==1&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==1&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.263157895 if RenumberedEvent==1&rwhite==1&sex==0
(39 real changes made)

. replace weight  = 1.596491228 if RenumberedEvent==1&rblack==1&sex==0
(3 real changes made)

. replace weight  = 0.957894737 if RenumberedEvent==1&rasian==1&sex==0
(5 real changes made)

. replace weight  = 0.821052632 if RenumberedEvent==1&rlatino==1&sex==0
(5 real changes made)

. replace weight  = 0.684210526 if RenumberedEvent==1&rother==1&sex==0
(4 real changes made)

. replace weight  = 0.684210526 if RenumberedEvent==1&rnative==1&sex==1
(5 real changes made)

. replace weight  = 1.026315789 if RenumberedEvent==1&rwhite==1&sex==1
(64 real changes made)

. replace weight  = 1.172932331 if RenumberedEvent==1&rblack==1&sex==1
(7 real changes made)

. replace weight  = 1.368421053 if RenumberedEvent==1&rasian==1&sex==1
(3 real changes made)

. replace weight  = 0.760233918 if RenumberedEvent==1&rlatino==1&sex==1
(9 real changes made)

. replace weight  = 0.821052632 if RenumberedEvent==1&rother==1&sex==1
(5 real changes made)

. 
. * Weights for Event 2: Women's March on Washington, DC, 2017
. 
. replace weight  = 0.843313946 if RenumberedEvent==2&sex==0&rwhite==.
(1 real change made)

. replace weight  = 1.010492333 if RenumberedEvent==2&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.794188862 if RenumberedEvent==2&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.017119069 if RenumberedEvent==2&sex==.&rwhite==1
(4 real changes made)

. replace weight  = 1.191283293 if RenumberedEvent==2&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.164810331 if RenumberedEvent==2&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.072154964 if RenumberedEvent==2&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.866387849 if RenumberedEvent==2&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.794188862 if RenumberedEvent==2&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.107684465 if RenumberedEvent==2&rwhite==1&sex==0
(38 real changes made)

. replace weight  = 1.588377724 if RenumberedEvent==2&rblack==1&sex==0
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==2&rasian==1&sex==0
(0 real changes made)

. replace weight  = 0.794188862 if RenumberedEvent==2&rlatino==1&sex==0
(4 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==2&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.794188862 if RenumberedEvent==2&rnative==1&sex==1
(4 real changes made)

. replace weight  = 0.999005990 if RenumberedEvent==2&rwhite==1&sex==1
(190 real changes made)

. replace weight  = 1.111864407 if RenumberedEvent==2&rblack==1&sex==1
(10 real changes made)

. replace weight  = 1.111864407 if RenumberedEvent==2&rasian==1&sex==1
(15 real changes made)

. replace weight  = 1.141646489 if RenumberedEvent==2&rlatino==1&sex==1
(16 real changes made)

. replace weight  = 0.866387849 if RenumberedEvent==2&rother==1&sex==1
(11 real changes made)

. 
. * Weights for Event 3: March for Life, Washington, DC, 2017
. 
. replace weight  = 0.876369327 if RenumberedEvent==3&sex==0&rwhite==.
(1 real change made)

. replace weight  = 1.166288002 if RenumberedEvent==3&sex==1&rwhite==.
(2 real changes made)

. replace weight  = 0.598290598 if RenumberedEvent==3&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.170340381 if RenumberedEvent==3&sex==.&rwhite==1
(1 real change made)

. replace weight  = 5.384615385 if RenumberedEvent==3&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.196581197 if RenumberedEvent==3&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.012491782 if RenumberedEvent==3&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.897435897 if RenumberedEvent==3&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.598290598 if RenumberedEvent==3&rnative==1&sex==0
(2 real changes made)

. replace weight  = 1.220512821 if RenumberedEvent==3&rwhite==1&sex==0
(50 real changes made)

. replace weight  = 3.290598291 if RenumberedEvent==3&rblack==1&sex==0
(2 real changes made)

. replace weight  = 1.495726496 if RenumberedEvent==3&rasian==1&sex==0
(2 real changes made)

. replace weight  = 0.747863248 if RenumberedEvent==3&rlatino==1&sex==0
(4 real changes made)

. replace weight  = 0.797720798 if RenumberedEvent==3&rother==1&sex==0
(3 real changes made)

. replace weight  = 0.598290598 if RenumberedEvent==3&rnative==1&sex==1
(1 real change made)

. replace weight  = 1.131143162 if RenumberedEvent==3&rwhite==1&sex==1
(64 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==3&rblack==1&sex==1
(0 real changes made)

. replace weight  = 0.897435897 if RenumberedEvent==3&rasian==1&sex==1
(2 real changes made)

. replace weight  = 1.130104463 if RenumberedEvent==3&rlatino==1&sex==1
(9 real changes made)

. replace weight  = 0.997150997 if RenumberedEvent==3&rother==1&sex==1
(3 real changes made)

. 
. * Weights for Event 4: Tax March on Washington, DC, 2017
. 
. replace weight  = 0.852796087 if RenumberedEvent==4&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.985519438 if RenumberedEvent==4&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.743315508 if RenumberedEvent==4&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.974725053 if RenumberedEvent==4&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.486631016 if RenumberedEvent==4&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.991087344 if RenumberedEvent==4&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.238859180 if RenumberedEvent==4&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 2.229946524 if RenumberedEvent==4&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==4&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.040641711 if RenumberedEvent==4&rwhite==1&sex==0
(3 real changes made)

. replace weight  = 1.238859180 if RenumberedEvent==4&rblack==1&sex==0
(35 real changes made)

. replace weight  = 0.743315508 if RenumberedEvent==4&rasian==1&sex==0
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==4&rlatino==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==4&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.743315508 if RenumberedEvent==4&rnative==1&sex==1
(4 real changes made)

. replace weight  = 0.942230926 if RenumberedEvent==4&rwhite==1&sex==1
(5 real changes made)

. replace weight  = 1.734402852 if RenumberedEvent==4&rblack==1&sex==1
(69 real changes made)

. replace weight  = 1.486631016 if RenumberedEvent==4&rasian==1&sex==1
(1 real change made)

. replace weight  = 0.991087344 if RenumberedEvent==4&rlatino==1&sex==1
(9 real changes made)

. replace weight  = 2.229946524 if RenumberedEvent==4&rother==1&sex==1
(1 real change made)

. 
. * Weights for Event 5: March for Science, Washington, DC, 2017
. 
. replace weight  = 0.955128205 if RenumberedEvent==5&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 1.009814398 if RenumberedEvent==5&sex==1&rwhite==.
(2 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.051537108 if RenumberedEvent==5&sex==.&rwhite==1
(4 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&sex==.&rblack==1
(1 real change made)

. replace weight  = 1.032951044 if RenumberedEvent==5&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.967913386 if RenumberedEvent==5&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&rnative==1&sex==0
(2 real changes made)

. replace weight  = 1.086028942 if RenumberedEvent==5&rwhite==1&sex==0
(111 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&rblack==1&sex==0
(6 real changes made)

. replace weight  = 1.143897638 if RenumberedEvent==5&rasian==1&sex==0
(10 real changes made)

. replace weight  = 0.953248031 if RenumberedEvent==5&rlatino==1&sex==0
(12 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==5&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&rnative==1&sex==1
(4 real changes made)

. replace weight  = 1.029147672 if RenumberedEvent==5&rwhite==1&sex==1
(171 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&rblack==1&sex==1
(8 real changes made)

. replace weight  = 0.947607511 if RenumberedEvent==5&rasian==1&sex==1
(13 real changes made)

. replace weight  = 0.989911417 if RenumberedEvent==5&rlatino==1&sex==1
(8 real changes made)

. replace weight  = 0.879921260 if RenumberedEvent==5&rother==1&sex==1
(6 real changes made)

. 
. * Weights for Event 6: People's Climate March, Washington, DC, 2017
. 
. replace weight  = 0.932980600 if RenumberedEvent==6&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.991096866 if RenumberedEvent==6&sex==1&rwhite==.
(2 real changes made)

. replace weight  = 0.851851852 if RenumberedEvent==6&sex==.&rnative==1
(1 real change made)

. replace weight  = 1.016414141 if RenumberedEvent==6&sex==.&rwhite==1
(3 real changes made)

. replace weight  = 1.224537037 if RenumberedEvent==6&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.993827160 if RenumberedEvent==6&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.973544974 if RenumberedEvent==6&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.277777778 if RenumberedEvent==6&sex==.&rother==1
(3 real changes made)

. replace weight  = 0.851851852 if RenumberedEvent==6&rnative==1&sex==0
(2 real changes made)

. replace weight  = 1.077054066 if RenumberedEvent==6&rwhite==1&sex==0
(87 real changes made)

. replace weight  = 1.490740741 if RenumberedEvent==6&rblack==1&sex==0
(4 real changes made)

. replace weight  = 1.135802469 if RenumberedEvent==6&rasian==1&sex==0
(6 real changes made)

. replace weight  = 0.958333333 if RenumberedEvent==6&rlatino==1&sex==0
(8 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==6&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.851851852 if RenumberedEvent==6&rnative==1&sex==1
(3 real changes made)

. replace weight  = 0.986608077 if RenumberedEvent==6&rwhite==1&sex==1
(177 real changes made)

. replace weight  = 1.135802469 if RenumberedEvent==6&rblack==1&sex==1
(12 real changes made)

. replace weight  = 0.851851852 if RenumberedEvent==6&rasian==1&sex==1
(6 real changes made)

. replace weight  = 0.993827160 if RenumberedEvent==6&rlatino==1&sex==1
(6 real changes made)

. replace weight  = 1.064814815 if RenumberedEvent==6&rother==1&sex==1
(4 real changes made)

. 
. * Weights for Event 7: Eqaulity March, Washington, DC, 2017
. 
. replace weight  = 0.934485360 if RenumberedEvent==7&sex==0&rwhite==.
(1 real change made)

. replace weight  = 0.906943450 if RenumberedEvent==7&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.712598425 if RenumberedEvent==7&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.978493360 if RenumberedEvent==7&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.247047244 if RenumberedEvent==7&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.306430446 if RenumberedEvent==7&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.282677165 if RenumberedEvent==7&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.712598425 if RenumberedEvent==7&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.712598425 if RenumberedEvent==7&rnative==1&sex==0
(2 real changes made)

. replace weight  = 1.078794838 if RenumberedEvent==7&rwhite==1&sex==0
(72 real changes made)

. replace weight  = 1.425196850 if RenumberedEvent==7&rblack==1&sex==0
(5 real changes made)

. replace weight  = 1.068897638 if RenumberedEvent==7&rasian==1&sex==0
(4 real changes made)

. replace weight  = 1.526996625 if RenumberedEvent==7&rlatino==1&sex==0
(7 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==7&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.712598425 if RenumberedEvent==7&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.862014224 if RenumberedEvent==7&rwhite==1&sex==1
(62 real changes made)

. replace weight  = 0.950131234 if RenumberedEvent==7&rblack==1&sex==1
(3 real changes made)

. replace weight  = 1.781496063 if RenumberedEvent==7&rasian==1&sex==1
(2 real changes made)

. replace weight  = 1.068897638 if RenumberedEvent==7&rlatino==1&sex==1
(8 real changes made)

. replace weight  = 0.712598425 if RenumberedEvent==7&rother==1&sex==1
(1 real change made)

. 
. * Weights for Event 8: Mother of All Rallies, Washington, DC, 2017
. 
. replace weight  = 0.842091601 if RenumberedEvent==8&sex==0&rwhite==.
(1 real change made)

. replace weight  = 0.998261595 if RenumberedEvent==8&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.492385787 if RenumberedEvent==8&sex==.&rnative==1
(1 real change made)

. replace weight  = 1.092593279 if RenumberedEvent==8&sex==.&rwhite==1
(2 real changes made)

. replace weight  = 1.336475707 if RenumberedEvent==8&sex==.&rblack==1
(1 real change made)

. replace weight  = 1.107868020 if RenumberedEvent==8&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.477157360 if RenumberedEvent==8&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.492385787 if RenumberedEvent==8&sex==.&rother==1
(2 real changes made)

. replace weight  = 0.492385787 if RenumberedEvent==8&rnative==1&sex==0
(5 real changes made)

. replace weight  = 1.118505985 if RenumberedEvent==8&rwhite==1&sex==0
(81 real changes made)

. replace weight  = 1.723350254 if RenumberedEvent==8&rblack==1&sex==0
(4 real changes made)

. replace weight  = 0.984771574 if RenumberedEvent==8&rasian==1&sex==0
(2 real changes made)

. replace weight  = 1.313028765 if RenumberedEvent==8&rlatino==1&sex==0
(6 real changes made)

. replace weight  = 0.492385787 if RenumberedEvent==8&rother==1&sex==0
(5 real changes made)

. replace weight  = 0.492385787 if RenumberedEvent==8&rnative==1&sex==1
(2 real changes made)

. replace weight  = 1.055112400 if RenumberedEvent==8&rwhite==1&sex==1
(56 real changes made)

. replace weight  = 0.820642978 if RenumberedEvent==8&rblack==1&sex==1
(3 real changes made)

. replace weight  = 1.230964467 if RenumberedEvent==8&rasian==1&sex==1
(2 real changes made)

. replace weight  = 1.969543147 if RenumberedEvent==8&rlatino==1&sex==1
(2 real changes made)

. replace weight  = 0.492385787 if RenumberedEvent==8&rother==1&sex==1
(7 real changes made)

. 
. * Weights for Event 9: Keep Your Promises Rally, Washington, DC, 2017
. 
. replace weight  = 0.822018815 if RenumberedEvent==9&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 1.018006148 if RenumberedEvent==9&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.589371981 if RenumberedEvent==9&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.113258186 if RenumberedEvent==9&sex==.&rwhite==1
(1 real change made)

. replace weight  = 0.589371981 if RenumberedEvent==9&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.982286634 if RenumberedEvent==9&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.841959972 if RenumberedEvent==9&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.128584644 if RenumberedEvent==9&rwhite==1&sex==0
(47 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rblack==1&sex==0
(1 real change made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rasian==1&sex==0
(1 real change made)

. replace weight  = 1.178743961 if RenumberedEvent==9&rlatino==1&sex==0
(2 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rother==1&sex==0
(7 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==9&rnative==1&sex==1
(0 real changes made)

. replace weight  = 1.096506011 if RenumberedEvent==9&rwhite==1&sex==1
(43 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rblack==1&sex==1
(2 real changes made)

. replace weight  = 1.178743961 if RenumberedEvent==9&rasian==1&sex==1
(2 real changes made)

. replace weight  = 0.707246377 if RenumberedEvent==9&rlatino==1&sex==1
(5 real changes made)

. replace weight  = 0.589371981 if RenumberedEvent==9&rother==1&sex==1
(3 real changes made)

. 
. * Weights for Event 10: March for Racial Justice, Washington, DC, 2017
. 
. replace weight  = 0.949587294 if RenumberedEvent==10&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.940169437 if RenumberedEvent==10&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.129852744 if RenumberedEvent==10&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.996928892 if RenumberedEvent==10&sex==.&rwhite==1
(2 real changes made)

. replace weight  = 1.129852744 if RenumberedEvent==10&sex==.&rblack==1
(3 real changes made)

. replace weight  = 1.016867470 if RenumberedEvent==10&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.932128514 if RenumberedEvent==10&sex==.&rlatino==1
(1 real change made)

. replace weight  = 0.847389558 if RenumberedEvent==10&sex==.&rother==1
(1 real change made)

. replace weight  = 1.694779116 if RenumberedEvent==10&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.151580682 if RenumberedEvent==10&rwhite==1&sex==0
(39 real changes made)

. replace weight  = 1.210556512 if RenumberedEvent==10&rblack==1&sex==0
(14 real changes made)

. replace weight  = 1.186345382 if RenumberedEvent==10&rasian==1&sex==0
(5 real changes made)

. replace weight  = 1.694779116 if RenumberedEvent==10&rlatino==1&sex==0
(1 real change made)

. replace weight  = 0.847389558 if RenumberedEvent==10&rother==1&sex==0
(2 real changes made)

. replace weight  = 0.847389558 if RenumberedEvent==10&rnative==1&sex==1
(2 real changes made)

. replace weight  = 0.934749307 if RenumberedEvent==10&rwhite==1&sex==1
(97 real changes made)

. replace weight  = 1.070386810 if RenumberedEvent==10&rblack==1&sex==1
(19 real changes made)

. replace weight  = 0.847389558 if RenumberedEvent==10&rasian==1&sex==1
(5 real changes made)

. replace weight  = 0.847389558 if RenumberedEvent==10&rlatino==1&sex==1
(8 real changes made)

. replace weight  = 0.847389558 if RenumberedEvent==10&rother==1&sex==1
(4 real changes made)

. 
. * Weights for Event 11: March for Life, Washington, DC, 2018
. 
. replace weight  = 1.403508772 if RenumberedEvent==11&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 1.151515152 if RenumberedEvent==11&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.225895317 if RenumberedEvent==11&sex==.&rwhite==1
(1 real change made)

. replace weight  = 2.424242424 if RenumberedEvent==11&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.606060606 if RenumberedEvent==11&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.818181818 if RenumberedEvent==11&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.401515152 if RenumberedEvent==11&rwhite==1&sex==0
(16 real changes made)

. replace weight  = 1.818181818 if RenumberedEvent==11&rblack==1&sex==0
(1 real change made)

. replace weight  = 0.606060606 if RenumberedEvent==11&rasian==1&sex==0
(1 real change made)

. replace weight  = 1.818181818 if RenumberedEvent==11&rlatino==1&sex==0
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==11&rother==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&rnative==1&sex==1
(0 real changes made)

. replace weight  = 1.125541126 if RenumberedEvent==11&rwhite==1&sex==1
(28 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&rblack==1&sex==1
(0 real changes made)

. replace weight  = 0.606060606 if RenumberedEvent==11&rasian==1&sex==1
(1 real change made)

. replace weight  = 1.818181818 if RenumberedEvent==11&rlatino==1&sex==1
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==11&rother==1&sex==1
(0 real changes made)

. 
. * Weights for Event 12: Women's March, Washington, DC, 2018
. 
. replace weight  = 1.154245409 if RenumberedEvent==12&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.978204553 if RenumberedEvent==12&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.800904977 if RenumberedEvent==12&sex==.&rnative==1
(1 real change made)

. replace weight  = 1.002659666 if RenumberedEvent==12&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.228054299 if RenumberedEvent==12&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.201357466 if RenumberedEvent==12&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.873714521 if RenumberedEvent==12&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.800904977 if RenumberedEvent==12&sex==.&rother==1
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==12&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.186525892 if RenumberedEvent==12&rwhite==1&sex==0
(27 real changes made)

. replace weight  = 1.601809955 if RenumberedEvent==12&rblack==1&sex==0
(2 real changes made)

. replace weight  = 0.800904977 if RenumberedEvent==12&rasian==1&sex==0
(2 real changes made)

. replace weight  = 0.800904977 if RenumberedEvent==12&rlatino==1&sex==0
(3 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==12&sex==0
(34 real changes made)

. replace weight  = 0.800904977 if RenumberedEvent==12&rnative==1&sex==1
(0 real changes made)

. replace weight  = 0.954925165 if RenumberedEvent==12&rwhite==1&sex==1
(104 real changes made)

. replace weight  = 1.170553428 if RenumberedEvent==12&rblack==1&sex==1
(13 real changes made)

. replace weight  = 1.601809955 if RenumberedEvent==12&rasian==1&sex==1
(2 real changes made)

. replace weight  = 0.901018100 if RenumberedEvent==12&rlatino==1&sex==1
(8 real changes made)

. replace weight  = 0.800904977 if RenumberedEvent==12&rother==1&sex==1
(3 real changes made)

. 
. * Weights for Event 13 -- Impeachment March, Washington, DC, 2018
. 
. replace weight  = 0.968978102 if RenumberedEvent==13&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 1.041289901 if RenumberedEvent==13&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.000235460 if RenumberedEvent==13&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.291970803 if RenumberedEvent==13&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.435523114 if RenumberedEvent==13&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rnative==1&sex==0
(1 real change made)

. replace weight  = 0.987977673 if RenumberedEvent==13&rwhite==1&sex==0
(34 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rblack==1&sex==0
(4 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rasian==1&sex==0
(1 real change made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rlatino==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==13&rother==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==13&rnative==1&sex==1
(0 real changes made)

. replace weight  = 1.007299270 if RenumberedEvent==13&rwhite==1&sex==1
(59 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==13&rblack==1&sex==1
(0 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rasian==1&sex==1
(2 real changes made)

. replace weight  = 0.861313869 if RenumberedEvent==13&rlatino==1&sex==1
(3 real changes made)

. replace weight  = 1.435523114 if RenumberedEvent==13&rother==1&sex==1
(3 real changes made)

. 
. * Weights for Event 14 -- March for Trump, Washington, DC, 2018
. 
. replace weight  = 1.057851240 if RenumberedEvent==14&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.969696970 if RenumberedEvent==14&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.272727273 if RenumberedEvent==14&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.333333333 if RenumberedEvent==14&rwhite==1&sex==0
(6 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rblack==1&sex==0
(0 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&rasian==1&sex==0
(1 real change made)

. replace weight  = 0.727272727 if RenumberedEvent==14&rlatino==1&sex==0
(2 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&rother==1&sex==0
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rnative==1&sex==1
(0 real changes made)

. replace weight  = 1.090909091 if RenumberedEvent==14&rwhite==1&sex==1
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rblack==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rasian==1&sex==1
(0 real changes made)

. replace weight  = 0.727272727 if RenumberedEvent==14&rlatino==1&sex==1
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==14&rother==1&sex==1
(0 real changes made)

. 
. * Weights for Event 15 -- March for Our Lives, Washington, DC, 2018
. 
. replace weight  = 1.116516517 if RenumberedEvent==15&sex==0&rwhite==.
(1 real change made)

. replace weight  = 0.993822394 if RenumberedEvent==15&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.159459459 if RenumberedEvent==15&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.029230317 if RenumberedEvent==15&sex==.&rwhite==1
(1 real change made)

. replace weight  = 1.174918919 if RenumberedEvent==15&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.927567568 if RenumberedEvent==15&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.927567568 if RenumberedEvent==15&sex==.&rlatino==1
(1 real change made)

. replace weight  = 1.030630631 if RenumberedEvent==15&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.772972973 if RenumberedEvent==15&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.130830831 if RenumberedEvent==15&rwhite==1&sex==0
(54 real changes made)

. replace weight  = 1.739189189 if RenumberedEvent==15&rblack==1&sex==0
(4 real changes made)

. replace weight  = 0.966216216 if RenumberedEvent==15&rasian==1&sex==0
(8 real changes made)

. replace weight  = 0.772972973 if RenumberedEvent==15&rlatino==1&sex==0
(4 real changes made)

. replace weight  = 0.772972973 if RenumberedEvent==15&rother==1&sex==0
(1 real change made)

. replace weight  = 1.545945946 if RenumberedEvent==15&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.987027027 if RenumberedEvent==15&rwhite==1&sex==1
(131 real changes made)

. replace weight  = 1.067438867 if RenumberedEvent==15&rblack==1&sex==1
(21 real changes made)

. replace weight  = 0.909379968 if RenumberedEvent==15&rasian==1&sex==1
(17 real changes made)

. replace weight  = 0.983783784 if RenumberedEvent==15&rlatino==1&sex==1
(12 real changes made)

. replace weight  = 1.159459459 if RenumberedEvent==15&rother==1&sex==1
(2 real changes made)

. 
. * Weights for Event 16 -- March for Science, Washington, DC, 2018 
. 
. replace weight  =  1.126408010 if RenumberedEvent==16&sex==0&rwhite==.
(1 real change made)

. replace weight  =  0.932469935 if RenumberedEvent==16&sex==1&rwhite==.
(3 real changes made)

. replace weight  =  0.765957447 if RenumberedEvent==16&sex==.&rnative==1
(0 real changes made)

. replace weight  =  0.995744681 if RenumberedEvent==16&sex==.&rwhite==1
(0 real changes made)

. replace weight  =  1.148936170 if RenumberedEvent==16&sex==.&rblack==1
(0 real changes made)

. replace weight  =  0.835589942 if RenumberedEvent==16&sex==.&rasian==1
(1 real change made)

. replace weight  =  1.531914894 if RenumberedEvent==16&sex==.&rlatino==1
(0 real changes made)

. replace weight  =  0.984802432 if RenumberedEvent==16&sex==.&rother==1
(0 real changes made)

. replace weight  =  1.000000000 if RenumberedEvent==16&rnative==1&sex==0
(0 real changes made)

. replace weight  =  1.114119923 if RenumberedEvent==16&rwhite==1&sex==0
(44 real changes made)

. replace weight  =  2.297872340 if RenumberedEvent==16&rblack==1&sex==0
(1 real change made)

. replace weight  =  0.765957447 if RenumberedEvent==16&rasian==1&sex==0
(4 real changes made)

. replace weight  =  2.297872340 if RenumberedEvent==16&rlatino==1&sex==0
(1 real change made)

. replace weight  =  0.765957447 if RenumberedEvent==16&rother==1&sex==0
(1 real change made)

. replace weight  =  1.000000000 if RenumberedEvent==16&rnative==1&sex==1
(0 real changes made)

. replace weight  =  0.916827853 if RenumberedEvent==16&rwhite==1&sex==1
(66 real changes made)

. replace weight  =  0.984802432 if RenumberedEvent==16&rblack==1&sex==1
(7 real changes made)

. replace weight  =  0.875379939 if RenumberedEvent==16&rasian==1&sex==1
(7 real changes made)

. replace weight  =  1.276595745 if RenumberedEvent==16&rlatino==1&sex==1
(3 real changes made)

. replace weight  =  1.021276596 if RenumberedEvent==16&rother==1&sex==1
(6 real changes made)

. 
. * Weights for Event 17 -- March for the Ocean, Washington, DC, 2018 
. 
. replace weight  = 1.144736842 if RenumberedEvent==17&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.943540670 if RenumberedEvent==17&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.000862813 if RenumberedEvent==17&sex==.&rwhite==1
(1 real change made)

. replace weight  = 0.953947368 if RenumberedEvent==17&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.526315789 if RenumberedEvent==17&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.221052632 if RenumberedEvent==17&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==17&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.162907268 if RenumberedEvent==17&rwhite==1&sex==0
(21 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&rblack==1&sex==0
(2 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&rasian==1&sex==0
(1 real change made)

. replace weight  = 1.526315789 if RenumberedEvent==17&rlatino==1&sex==0
(1 real change made)

. replace weight  = 1.526315789 if RenumberedEvent==17&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&rnative==1&sex==1
(2 real changes made)

. replace weight  = 0.915789474 if RenumberedEvent==17&rwhite==1&sex==1
(40 real changes made)

. replace weight  = 1.144736842 if RenumberedEvent==17&rblack==1&sex==1
(2 real changes made)

. replace weight  = 0.763157895 if RenumberedEvent==17&rasian==1&sex==1
(5 real changes made)

. replace weight  = 1.526315789 if RenumberedEvent==17&rlatino==1&sex==1
(2 real changes made)

. replace weight  = 1.144736842 if RenumberedEvent==17&rother==1&sex==1
(4 real changes made)

. 
. * Weights for Event 18 -- Families Belong Together, Washington, DC, 2018 
. 
. replace weight  = 1.062326389 if RenumberedEvent==18&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.984224319 if RenumberedEvent==18&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.879166667 if RenumberedEvent==18&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.983567708 if RenumberedEvent==18&sex==.&rwhite==1
(2 real changes made)

. replace weight  = 1.067559524 if RenumberedEvent==18&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.172222222 if RenumberedEvent==18&sex==.&rasian==1
(1 real change made)

. replace weight  = 1.004761905 if RenumberedEvent==18&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.055000000 if RenumberedEvent==18&sex==.&rother==1
(1 real change made)

. replace weight  = 0.879166667 if RenumberedEvent==18&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.036965812 if RenumberedEvent==18&rwhite==1&sex==0
(39 real changes made)

. replace weight  = 1.172222222 if RenumberedEvent==18&rblack==1&sex==0
(3 real changes made)

. replace weight  = 1.172222222 if RenumberedEvent==18&rasian==1&sex==0
(3 real changes made)

. replace weight  = 0.879166667 if RenumberedEvent==18&rlatino==1&sex==0
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==18&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.879166667 if RenumberedEvent==18&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.966356749 if RenumberedEvent==18&rwhite==1&sex==1
(121 real changes made)

. replace weight  = 1.039015152 if RenumberedEvent==18&rblack==1&sex==1
(11 real changes made)

. replace weight  = 1.172222222 if RenumberedEvent==18&rasian==1&sex==1
(9 real changes made)

. replace weight  = 1.025694444 if RenumberedEvent==18&rlatino==1&sex==1
(12 real changes made)

. replace weight  = 0.879166667 if RenumberedEvent==18&rother==1&sex==1
(5 real changes made)

. 
. * Weights for Event 19 -- Youth Climate March, Washington, DC, 2018 
. 
. replace weight  = 1.258541090 if RenumberedEvent==19&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.896262395 if RenumberedEvent==19&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.923976608 if RenumberedEvent==19&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.924508240 if RenumberedEvent==19&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.236842105 if RenumberedEvent==19&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.236842105 if RenumberedEvent==19&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.236842105 if RenumberedEvent==19&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==19&sex==.&rother==1
(0 real changes made)

. replace weight  = 2.061403509 if RenumberedEvent==19&rnative==1&sex==0
(2 real changes made)

. replace weight  = 0.989473684 if RenumberedEvent==19&rwhite==1&sex==0
(15 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==19&rblack==1&sex==0
(0 real changes made)

. replace weight  = 1.649122807 if RenumberedEvent==19&rasian==1&sex==0
(1 real change made)

. replace weight  = 1.649122807 if RenumberedEvent==19&rlatino==1&sex==0
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==19&rother==1&sex==0
(0 real changes made)

. replace weight  = 1.649122807 if RenumberedEvent==19&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.870370370 if RenumberedEvent==19&rwhite==1&sex==1
(18 real changes made)

. replace weight  = 0.824561404 if RenumberedEvent==19&rblack==1&sex==1
(2 real changes made)

. replace weight  = 0.824561404 if RenumberedEvent==19&rasian==1&sex==1
(1 real change made)

. replace weight  = 0.824561404 if RenumberedEvent==19&rlatino==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==19&rother==1&sex==1
(0 real changes made)

. 
. * Weights for Event 20 -- Unite the Right, Washington, DC, 2018 
. 
. replace weight  = 1.000000000 if RenumberedEvent==20&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&sex==.&rother==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rwhite==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rblack==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rasian==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rlatino==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rother==1&sex==0
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rnative==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rwhite==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rblack==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rasian==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rlatino==1&sex==1
(0 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==20&rother==1&sex==1
(0 real changes made)

. 
. * Weights for Event 21 -- Still Here, Still Strong, Washington, DC, 2018
. 
. replace weight  = 1.215217391 if RenumberedEvent==21&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.904347826 if RenumberedEvent==21&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.747826087 if RenumberedEvent==21&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.974844720 if RenumberedEvent==21&sex==.&rwhite==1
(5 real changes made)

. replace weight  = 1.346086957 if RenumberedEvent==21&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.495652174 if RenumberedEvent==21&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.046956522 if RenumberedEvent==21&sex==.&rlatino==1
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==21&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.747826087 if RenumberedEvent==21&rnative==1&sex==0
(1 real change made)

. replace weight  = 1.139544513 if RenumberedEvent==21&rwhite==1&sex==0
(21 real changes made)

. replace weight  = 1.371014493 if RenumberedEvent==21&rblack==1&sex==0
(6 real changes made)

. replace weight  = 1.495652174 if RenumberedEvent==21&rasian==1&sex==0
(1 real change made)

. replace weight  = 1.246376812 if RenumberedEvent==21&rlatino==1&sex==0
(3 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==21&rother==1&sex==0
(0 real changes made)

. replace weight  = 0.747826087 if RenumberedEvent==21&rnative==1&sex==1
(2 real changes made)

. replace weight  = 0.876024845 if RenumberedEvent==21&rwhite==1&sex==1
(35 real changes made)

. replace weight  = 1.308695652 if RenumberedEvent==21&rblack==1&sex==1
(4 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==21&rasian==1&sex==1
(0 real changes made)

. replace weight  = 0.747826087 if RenumberedEvent==21&rlatino==1&sex==1
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==21&rother==1&sex==1
(0 real changes made)

. 
. * Weights for Event 22 -- Rise Up, Fight Back, Washington, DC, 2018
. 
. replace weight  = 1.105978261 if RenumberedEvent==22&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.930147059 if RenumberedEvent==22&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&sex==.&rnative==1
(0 real changes made)

. replace weight  = 0.982142857 if RenumberedEvent==22&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 2.062500000 if RenumberedEvent==22&sex==.&rblack==1
(0 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.145833333 if RenumberedEvent==22&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&sex==.&rother==1
(1 real change made)

. replace weight  = 0.687500000 if RenumberedEvent==22&rnative==1&sex==0
(1 real change made)

. replace weight  = 0.993055556 if RenumberedEvent==22&rwhite==1&sex==0
(18 real changes made)

. replace weight  = 4.125000000 if RenumberedEvent==22&rblack==1&sex==0
(1 real change made)

. replace weight  = 1.000000000 if RenumberedEvent==22&rasian==1&sex==0
(0 real changes made)

. replace weight  = 1.375000000 if RenumberedEvent==22&rlatino==1&sex==0
(1 real change made)

. replace weight  = 0.687500000 if RenumberedEvent==22&rother==1&sex==0
(2 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.962500000 if RenumberedEvent==22&rwhite==1&sex==1
(10 real changes made)

. replace weight  = 1.031250000 if RenumberedEvent==22&rblack==1&sex==1
(2 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&rasian==1&sex==1
(1 real change made)

. replace weight  = 1.031250000 if RenumberedEvent==22&rlatino==1&sex==1
(2 real changes made)

. replace weight  = 0.687500000 if RenumberedEvent==22&rother==1&sex==1
(1 real change made)

. 
. * Weights for Event 23 -- Mother of All Rallies, Washington, DC, 2018, Washington, DC, 2018
. 
. replace weight  = 1.111111111 if RenumberedEvent==23&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.900469202 if RenumberedEvent==23&sex==1&rwhite==.
(0 real changes made)

. replace weight  = 0.582010582 if RenumberedEvent==23&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.039304611 if RenumberedEvent==23&sex==.&rwhite==1
(1 real change made)

. replace weight  = 1.455026455 if RenumberedEvent==23&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.358024691 if RenumberedEvent==23&sex==.&rasian==1
(0 real changes made)

. replace weight  = 1.047619048 if RenumberedEvent==23&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.776014109 if RenumberedEvent==23&sex==.&rother==1
(0 real changes made)

. replace weight  = 0.582010582 if RenumberedEvent==23&rnative==1&sex==0
(2 real changes made)

. replace weight  = 1.112286890 if RenumberedEvent==23&rwhite==1&sex==0
(24 real changes made)

. replace weight  = 1.164021164 if RenumberedEvent==23&rblack==1&sex==0
(0 real changes made)

. replace weight  = 1.455026455 if RenumberedEvent==23&rasian==1&sex==0
(0 real changes made)

. replace weight  = 1.746031746 if RenumberedEvent==23&rlatino==1&sex==0
(0 real changes made)

. replace weight  = 1.164021164 if RenumberedEvent==23&rother==1&sex==0
(2 real changes made)

. replace weight  = 0.582010582 if RenumberedEvent==23&rnative==1&sex==1
(1 real change made)

. replace weight  = 0.955094288 if RenumberedEvent==23&rwhite==1&sex==1
(18 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==23&rblack==1&sex==1
(0 real changes made)

. replace weight  = 1.164021164 if RenumberedEvent==23&rasian==1&sex==1
(1 real change made)

. replace weight  = 0.873015873 if RenumberedEvent==23&rlatino==1&sex==1
(1 real change made)

. replace weight  = 0.582010582 if RenumberedEvent==23&rother==1&sex==1
(1 real change made)

. 
. * Weights for Event 24 -- Women's March on the Pentagon, Washington, DC, 2018, Washington, DC, 2018
. 
. replace weight  = 1.106319978 if RenumberedEvent==24&sex==0&rwhite==.
(0 real changes made)

. replace weight  = 0.926067307 if RenumberedEvent==24&sex==1&rwhite==.
(1 real change made)

. replace weight  = 0.868713841 if RenumberedEvent==24&sex==.&rnative==1
(0 real changes made)

. replace weight  = 1.013499481 if RenumberedEvent==24&sex==.&rwhite==1
(0 real changes made)

. replace weight  = 1.114849429 if RenumberedEvent==24&sex==.&rblack==1
(0 real changes made)

. replace weight  = 1.520249221 if RenumberedEvent==24&sex==.&rasian==1
(0 real changes made)

. replace weight  = 0.923008456 if RenumberedEvent==24&sex==.&rlatino==1
(0 real changes made)

. replace weight  = 0.844582901 if RenumberedEvent==24&sex==.&rother==1
(1 real change made)

. replace weight  = 1.140186916 if RenumberedEvent==24&rnative==1&sex==0
(0 real changes made)

. replace weight  = 1.119072343 if RenumberedEvent==24&rwhite==1&sex==0
(22 real changes made)

. replace weight  = 1.182416061 if RenumberedEvent==24&rblack==1&sex==0
(3 real changes made)

. replace weight  = 0.950155763 if RenumberedEvent==24&rasian==1&sex==0
(2 real changes made)

. replace weight  = 1.013499481 if RenumberedEvent==24&rlatino==1&sex==0
(3 real changes made)

. replace weight  = 1.064174455 if RenumberedEvent==24&rother==1&sex==0
(2 real changes made)

. replace weight  = 0.760124611 if RenumberedEvent==24&rnative==1&sex==1
(2 real changes made)

. replace weight  = 0.936719217 if RenumberedEvent==24&rwhite==1&sex==1
(41 real changes made)

. replace weight  = 1.013499481 if RenumberedEvent==24&rblack==1&sex==1
(2 real changes made)

. replace weight  = 1.000000000 if RenumberedEvent==24&rasian==1&sex==1
(0 real changes made)

. replace weight  = 0.880144286 if RenumberedEvent==24&rlatino==1&sex==1
(8 real changes made)

. replace weight  = 0.760124611 if RenumberedEvent==24&rother==1&sex==1
(7 real changes made)

. 
. * Set Survey Weights
. 
. svyset [pweight=weight]

      pweight: weight
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. 
. * Figure 1: Where did BLM activists originate their activism?
. 
. svy: mean FE_BLM
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =      3,009
Number of PSUs   =   3,009        Population size = 3,145.1252
                                  Design df       =      3,008

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      FE_BLM |   .0344211   .0036669      .0272313     .041611
--------------------------------------------------------------

. svy: mean FE_BLM if blm ==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        691
Number of PSUs   =     691        Population size = 721.355544
                                  Design df       =        690

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      FE_BLM |   .1500768   .0148583      .1209039    .1792497
--------------------------------------------------------------

. 
. svy: mean CaseID if FE_ProBlackAntiRacism == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        112
Number of PSUs   =     112        Population size =  116.09877
                                  Design df       =        111

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09   50187.47      2.02e+09    2.02e+09
--------------------------------------------------------------

. svy: mean CaseID if FE_Peace_Antiwar == 1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        503
Number of PSUs   =     503        Population size = 523.243427
                                  Design df       =        502

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09   23369.42      2.02e+09    2.02e+09
--------------------------------------------------------------

. svy: mean CaseID if FE_Science == 1 | FE_OccupyInequality == 1 | FE_GlobalJustice == 1 | FE_ProImmigRefugees == 1 | /*
>         */ FE_LGBT_HIV == 1 | FE_Labor == 1 | FE_PCanditate_AntiCCandidate == 1 | FE_OProgressive == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        553
Number of PSUs   =     553        Population size = 576.471373
                                  Design df       =        552

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09   22268.27      2.02e+09    2.02e+09
--------------------------------------------------------------

. svy: mean CaseID if FE_Environment == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        155
Number of PSUs   =     155        Population size = 157.507788
                                  Design df       =        154

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09   39313.14      2.02e+09    2.02e+09
--------------------------------------------------------------

. svy: mean CaseID if FE_Womens_ProAbortion == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        448
Number of PSUs   =     448        Population size = 461.212527
                                  Design df       =        447

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09    24495.1      2.02e+09    2.02e+09
--------------------------------------------------------------

. svy: mean CaseID if FE_ProLife == 1 | FE_OConservative == 1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        296
Number of PSUs   =     296        Population size = 320.798609
                                  Design df       =        295

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      CaseID |   2.02e+09   25098.94      2.02e+09    2.02e+09
--------------------------------------------------------------

. 
. svy: mean blm if FE_ProBlackAntiRacism == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        112
Number of PSUs   =     112        Population size =  116.09877
                                  Design df       =        111

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |   .4422215   .0476369      .3478258    .5366172
--------------------------------------------------------------

. svy: mean blm if FE_Peace_Antiwar == 1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        503
Number of PSUs   =     503        Population size = 523.243427
                                  Design df       =        502

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |   .3641759   .0218688      .3212103    .4071415
--------------------------------------------------------------

. svy: mean blm if FE_Science == 1 | FE_OccupyInequality == 1 | FE_GlobalJustice == 1 | FE_ProImmigRefugees == 1 | /*
>         */ FE_LGBT_HIV == 1 | FE_Labor == 1 | FE_PCanditate_AntiCCandidate == 1 | FE_OProgressive == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        548
Number of PSUs   =     548        Population size = 570.922601
                                  Design df       =        547

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |   .3265406   .0202529      .2867576    .3663236
--------------------------------------------------------------

. svy: mean blm if FE_Environment == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        153
Number of PSUs   =     153        Population size = 155.444126
                                  Design df       =        152

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |   .2780133   .0368756      .2051583    .3508683
--------------------------------------------------------------

. svy: mean blm if FE_Womens_ProAbortion == 1 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        443
Number of PSUs   =     443        Population size = 456.058196
                                  Design df       =        442

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |    .239367   .0203471      .1993779     .279356
--------------------------------------------------------------

. svy: mean blm if FE_ProLife == 1 | FE_OConservative == 1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       1        Number of obs   =        291
Number of PSUs   =     291        Population size = 315.654089
                                  Design df       =        290

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
         blm |   .0592825   .0158216      .0281429    .0904221
--------------------------------------------------------------

. 
. sum blm if FE_ProBlackAntiRacism == 1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        112    .4464286    .4993561          0          1

. sum blm if FE_Peace_Antiwar == 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        503     .359841    .4804314          0          1

. sum blm if FE_Science == 1 | FE_OccupyInequality == 1 | FE_GlobalJustice == 1 | FE_ProImmigRefugees == 1 | /*
>         */ FE_LGBT_HIV == 1 | FE_Labor == 1 | FE_PCanditate_AntiCCandidate == 1 | FE_OProgressive == 1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        548     .330292    .4707479          0          1

. sum blm if FE_Environment == 1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        153    .2745098    .4477325          0          1

. sum blm if FE_Womens_ProAbortion == 1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        443    .2460497    .4311948          0          1

. sum blm if FE_ProLife == 1 | FE_OConservative == 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        291    .0549828    .2283394          0          1

. 
. * Table 2: Demographics and Identities
. 
. impute rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_rblack)
  4.36% (142) observations imputed

. replace i_rblack = 0 if i_rblack < 0 & blm !=.
(5 real changes made)

. replace i_rblack = 1 if i_rblack > 1 & blm !=.
(0 real changes made)

. impute rlatino rblack rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_rlatino)
  4.36% (142) observations imputed

. replace i_rlatino = 0 if i_rlatino < 0 & blm !=.
(1 real change made)

. replace i_rlatino = 1 if i_rlatino > 1 & blm !=.
(0 real changes made)

. impute rasian rblack rlatino rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_rasian)
  4.36% (142) observations imputed

. replace i_rasian = 0 if i_rasian < 0 & blm !=.
(3 real changes made)

. replace i_rasian = 1 if i_rasian > 1 & blm !=.
(0 real changes made)

. impute rother rblack rlatino rasian female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_rother)
  4.39% (143) observations imputed

. replace i_rother = 0 if i_rother < 0 & blm !=.
(0 real changes made)

. replace i_rother = 1 if i_rother > 1 & blm !=.
(0 real changes made)

. impute female rblack rlatino rasian rother age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_female)
  5.34% (174) observations imputed

. replace i_female = 0 if i_female < 0 & blm !=.
(0 real changes made)

. replace i_female = 1 if i_female > 1 & blm !=.
(0 real changes made)

. impute age_numeric rblack rlatino rasian rother female educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_age_numeric)
  6.78% (221) observations imputed

. replace i_age_numeric = 0 if i_age_numeric < 0 & blm !=.
(0 real changes made)

. impute educ rblack rlatino rasian rother female age_numeric income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_educ)
  4.66% (152) observations imputed

. replace i_educ = 1 if i_educ < 1 & blm !=.
(0 real changes made)

. replace i_educ = 6 if i_educ > 6 & blm !=.
(0 real changes made)

. impute income rblack rlatino rasian rother female age_numeric educ logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_income)
 10.43% (340) observations imputed

. replace i_income = 0 if i_income < 0 & blm !=.
(13 real changes made)

. impute logdistance rblack rlatino rasian rother female age_numeric educ income r_ideo pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_logdistance)
  9.02% (294) observations imputed

. replace i_logdistance = 0 if i_logdistance < 0 & blm !=.
(0 real changes made)

. impute r_ideo rblack rlatino rasian rother female age_numeric educ income logdistance pid_folded member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_r_ideo)
  8.87% (289) observations imputed

. replace i_r_ideo = 1 if i_r_ideo < 1 & blm !=.
(0 real changes made)

. replace i_r_ideo = 9 if i_r_ideo > 9 & blm !=.
(0 real changes made)

. impute pid_folded rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo member r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_pid_folded)
 13.75% (448) observations imputed

. replace i_pid_folded = 0 if i_pid_folded < 0 & blm !=.
(0 real changes made)

. replace i_pid_folded = 3 if i_pid_folded > 3 & blm !=.
(0 real changes made)

. impute member rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded r_aidimp r_know Conservative_Event Month if blm !=
> ., gen(i_member)
  2.92% (95) observations imputed

. replace i_member = 1 if i_member > 1 & blm !=.
(0 real changes made)

. replace i_member = 0 if i_member < 0 & blm !=.
(2 real changes made)

. impute r_aidimp rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_know Conservative_Event Month if blm !=
> ., gen(i_r_aidimp)
  4.11% (134) observations imputed

. replace i_r_aidimp = 1 if i_r_aidimp < 1 & blm !=.
(0 real changes made)

. replace i_r_aidimp = 4 if i_r_aidimp > 4 & blm !=.
(0 real changes made)

. impute r_know rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp Conservative_Event Month if blm !=
> ., gen(i_r_know)
  5.92% (193) observations imputed

. replace i_r_know = 1 if i_r_know < 1 & blm !=.
(0 real changes made)

. replace i_r_know = 3 if i_r_know > 3 & blm !=.
(0 real changes made)

. 
. svyset [pweight=weight], strata(RenumberedEvent)

      pweight: weight
          VCE: linearized
  Single unit: missing
     Strata 1: RenumberedEvent
         SU 1: <observations>
        FPC 1: <zero>

. 
. svy: mean i_rblack if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rblack |   .1700583          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_rlatino if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   i_rlatino |   .0944218          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_rasian if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rasian |   .0527032          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_rother if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rother |   .0400951          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_female if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_female |   .6619456          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_age_numeric if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
i_age_numeric |   38.87905          .             .           .
---------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_educ if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      i_educ |   4.607648          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_income if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_income |   76.97541          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_logdistance if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
i_logdistance |   3.939602          .             .           .
---------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_r_ideo if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_r_ideo |   7.757996          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_pid_folded if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
i_pid_folded |   2.092573          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_member if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_member |   .5963994          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_r_aidimp if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  i_r_aidimp |   3.173241          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. svy: mean i_r_know if blm==1
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      22        Number of obs   =        937
Number of PSUs   =     937        Population size = 973.896831
                                  Design df       =        915

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_r_know |   1.887373          .             .           .
--------------------------------------------------------------
Note: Missing standard error because of stratum with single
      sampling unit.

. 
. sum blm i_rblack i_rlatino i_rasian i_rother i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ i_r_ideo i_pid_folded i_member i_r_aidimp i_r_know Conservative_Event Month if blm==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |        937           1           0          1          1
    i_rblack |        937    .1414846    .3424415          0          1
   i_rlatino |        937    .0954102    .2882675          0          1
    i_rasian |        937    .0516474    .2159545          0          1
    i_rother |        937     .047344    .2091616          0          1
-------------+---------------------------------------------------------
    i_female |        937    .6873956    .4497312          0          1
i_age_nume~c |        937    39.08409    16.18138         14         87
      i_educ |        937    4.625403    1.303742          1          6
    i_income |        937    77.59422    74.70511          0        450
i_logdista~e |        937    3.941115    2.131618          0   8.629146
-------------+---------------------------------------------------------
    i_r_ideo |        937    7.780149     1.37726          1          9
i_pid_folded |        937    2.085967     1.00515          0          3
    i_member |        937    .6038209    .4828925          0          1
  i_r_aidimp |        937    3.185972    .8457868          1          4
    i_r_know |        937    1.894344    .7110243          1          3
-------------+---------------------------------------------------------
Conservati~t |        937    .0160085    .1255751          0          1
       Month |        937    9.010672    7.063047          0         21

. 
. svy: mean i_rblack if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rblack |    .095873   .0047896      .0864807    .1052654
--------------------------------------------------------------

. svy: mean i_rlatino if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   i_rlatino |   .0545611   .0046607      .0454215    .0637007
--------------------------------------------------------------

. svy: mean i_rasian if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rasian |   .0474669   .0043239      .0389878     .055946
--------------------------------------------------------------

. svy: mean i_rother if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_rother |   .0292568   .0032452       .022893    .0356206
--------------------------------------------------------------

. svy: mean i_female if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_female |   .5955744   .0098544        .57625    .6148988
--------------------------------------------------------------

. svy: mean i_age_numeric if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
i_age_numeric |   44.45066   .3370086      43.78979    45.11153
---------------------------------------------------------------

. svy: mean i_educ if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
      i_educ |   4.529359    .028351      4.473763    4.584955
--------------------------------------------------------------

. svy: mean i_income if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_income |   97.55251   1.875558      93.87455    101.2305
--------------------------------------------------------------

. svy: mean i_logdistance if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

---------------------------------------------------------------
              |             Linearized
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
i_logdistance |   4.526129   .0381188      4.451379     4.60088
---------------------------------------------------------------

. svy: mean i_r_ideo if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_r_ideo |   6.454026   .0318935      6.391483    6.516569
--------------------------------------------------------------

. svy: mean i_pid_folded if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
i_pid_folded |   2.030051    .021152      1.988572     2.07153
--------------------------------------------------------------

. svy: mean i_member if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_member |   .4335862   .0100104       .413956    .4532165
--------------------------------------------------------------

. svy: mean i_r_aidimp if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  i_r_aidimp |   2.682869   .0199505      2.643746    2.721992
--------------------------------------------------------------

. svy: mean i_r_know if blm==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,322
Number of PSUs   =   2,322        Population size = 2,428.1075
                                  Design df       =      2,298

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    i_r_know |    1.74885   .0143448       1.72072    1.776981
--------------------------------------------------------------

. 
. sum blm i_rblack i_rlatino i_rasian i_rother i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ i_r_ideo i_pid_folded i_member i_r_aidimp i_r_know Conservative_Event Month if blm==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |      2,322           0           0          0          0
    i_rblack |      2,322    .0777026    .2616665          0          1
   i_rlatino |      2,322    .0555797    .2230052          0          1
    i_rasian |      2,322    .0490218    .2113857          0          1
    i_rother |      2,322    .0358096    .1812522          0          1
-------------+---------------------------------------------------------
    i_female |      2,322    .6119258    .4757963          0          1
i_age_nume~c |      2,322    44.30318    16.90223         13        108
      i_educ |      2,322    4.525429    1.421694          1          6
    i_income |      2,322    97.18302    90.87016          0        450
i_logdista~e |      2,322    4.517437    1.926609          0   8.883502
-------------+---------------------------------------------------------
    i_r_ideo |      2,322    6.488755     2.29381          1          9
i_pid_folded |      2,322    2.031833    1.020159          0          3
    i_member |      2,322    .4344689    .4895445          0          1
  i_r_aidimp |      2,322    2.693701    .9522271          1          4
    i_r_know |      2,322    1.751521    .7177261          1          3
-------------+---------------------------------------------------------
Conservati~t |      2,322    .2166236    .4120327          0          1
       Month |      2,322    7.968562    6.314148          0         21

. 
. svy: reg i_rblack blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      23.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0112

------------------------------------------------------------------------------
             |             Linearized
    i_rblack |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0741853   .0152275     4.87   0.000     .0443288    .1040418
       _cons |    .095873    .005438    17.63   0.000     .0852108    .1065352
------------------------------------------------------------------------------

. svy: reg i_rlatino blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      13.91
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0055

------------------------------------------------------------------------------
             |             Linearized
   i_rlatino |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0398607   .0106865     3.73   0.000     .0189078    .0608136
       _cons |   .0545611   .0046655    11.69   0.000     .0454135    .0637087
------------------------------------------------------------------------------

. svy: reg i_rasian blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =       0.38
                                                Prob > F          =     0.5402
                                                R-squared         =     0.0001

------------------------------------------------------------------------------
             |             Linearized
    i_rasian |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0052364    .008549     0.61   0.540    -.0115257    .0219984
       _cons |   .0474669   .0043245    10.98   0.000     .0389878    .0559459
------------------------------------------------------------------------------

. svy: reg i_rother blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =       2.59
                                                Prob > F          =     0.1079
                                                R-squared         =     0.0008

------------------------------------------------------------------------------
             |             Linearized
    i_rother |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0108383   .0067397     1.61   0.108    -.0023763    .0240529
       _cons |   .0292568   .0032453     9.02   0.000     .0228937    .0356199
------------------------------------------------------------------------------

. svy: reg i_female blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      12.63
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0040

------------------------------------------------------------------------------
             |             Linearized
    i_female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0663712   .0186782     3.55   0.000      .029749    .1029935
       _cons |   .5955744   .0099171    60.06   0.000     .5761299    .6150189
------------------------------------------------------------------------------

. svy: reg i_age_numeric blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      77.38
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0222

------------------------------------------------------------------------------
             |             Linearized
i_age_nume~c |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |  -5.571612    .633401    -8.80   0.000     -6.81352   -4.329704
       _cons |   44.45066   .3405824   130.51   0.000     43.78288    45.11844
------------------------------------------------------------------------------

. svy: reg i_educ blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =       2.21
                                                Prob > F          =     0.1372
                                                R-squared         =     0.0006

------------------------------------------------------------------------------
             |             Linearized
      i_educ |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |    .078289   .0526575     1.49   0.137    -.0249565    .1815345
       _cons |   4.529359   .0285791   158.48   0.000     4.473324    4.585394
------------------------------------------------------------------------------

. svy: reg i_income blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      44.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0115

------------------------------------------------------------------------------
             |             Linearized
    i_income |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   -20.5771   3.097186    -6.64   0.000    -26.64974   -14.50445
       _cons |   97.55251   1.881005    51.86   0.000     93.86443    101.2406
------------------------------------------------------------------------------

. svy: reg i_logdistance blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      52.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0175

------------------------------------------------------------------------------
             |             Linearized
i_logdista~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |  -.5865272   .0812591    -7.22   0.000    -.7458518   -.4272027
       _cons |   4.526129   .0388113   116.62   0.000     4.450032    4.602227
------------------------------------------------------------------------------

. svy: reg i_r_ideo blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =     498.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0739

------------------------------------------------------------------------------
             |             Linearized
    i_r_ideo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |    1.30397   .0584266    22.32   0.000     1.189413    1.418527
       _cons |   6.454026   .0339195   190.27   0.000      6.38752    6.520532
------------------------------------------------------------------------------

. svy: reg i_pid_folded blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =       2.55
                                                Prob > F          =     0.1104
                                                R-squared         =     0.0008

------------------------------------------------------------------------------
             |             Linearized
i_pid_folded |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .0625212   .0391503     1.60   0.110    -.0142407    .1392831
       _cons |   2.030051   .0212402    95.58   0.000     1.988406    2.071697
------------------------------------------------------------------------------

. svy: reg i_member blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      72.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0223

------------------------------------------------------------------------------
             |             Linearized
    i_member |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .1628132   .0191213     8.51   0.000     .1253221    .2003043
       _cons |   .4335862   .0100612    43.09   0.000     .4138592    .4533132
------------------------------------------------------------------------------

. svy: reg i_r_aidimp blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =     195.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0540

------------------------------------------------------------------------------
             |             Linearized
  i_r_aidimp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .4903725   .0350399    13.99   0.000     .4216699    .5590751
       _cons |   2.682869    .020004   134.12   0.000     2.643647     2.72209
------------------------------------------------------------------------------

. svy: reg i_r_know blm
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(   1,   3235)   =      24.57
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0076

------------------------------------------------------------------------------
             |             Linearized
    i_r_know |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         blm |   .1385225   .0279461     4.96   0.000     .0837287    .1933163
       _cons |    1.74885   .0145421   120.26   0.000     1.720338    1.777363
------------------------------------------------------------------------------

. 
. sum blm i_rblack i_rlatino i_rasian i_rother i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ i_r_ideo i_pid_folded i_member i_r_aidimp i_r_know Conservative_Event Month

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         blm |      3,259    .2875115    .4526715          0          1
    i_rblack |      3,259    .0960407     .288619          0          1
   i_rlatino |      3,259    .0670314    .2441868          0          1
    i_rasian |      3,259    .0497767    .2126794          0          1
    i_rother |      3,259    .0391259    .1897365          0          1
-------------+---------------------------------------------------------
    i_female |      3,259    .6336243    .4696266          0          1
i_age_nume~c |      3,259    42.80263    16.86202         13        108
      i_educ |      3,259    4.554172    1.389347          1          6
    i_income |      3,259    91.55102    86.97432          0        450
i_logdista~e |      3,259    4.351738    2.004436          0   8.883502
-------------+---------------------------------------------------------
    i_r_ideo |      3,259    6.860046    2.152909          1          9
i_pid_folded |      3,259    2.047397    1.016008          0          3
    i_member |      3,259    .4831595    .4935572          0          1
  i_r_aidimp |      3,259    2.835235    .9492798          1          4
    i_r_know |      3,259    1.792584    .7186109          1          3
-------------+---------------------------------------------------------
Conservati~t |      3,860    .1823834    .3862102          0          1
       Month |      3,860    7.971762    6.551131          0         21

. 
. svy: probit blm i_rblack i_rlatino i_rasian i_rother i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ i_r_ideo i_pid_folded i_member i_r_aidimp i_r_know Conservative_Event Month
(running probit on estimation sample)

Survey: Probit regression

Number of strata   =        24                  Number of obs     =      3,259
Number of PSUs     =     3,259                  Population size   = 3,402.0044
                                                Design df         =      3,235
                                                F(  16,   3220)   =      30.27
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------------
                   |             Linearized
               blm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
          i_rblack |   .4144853   .0870083     4.76   0.000     .2438884    .5850822
         i_rlatino |   .3531229   .1067043     3.31   0.001     .1439081    .5623378
          i_rasian |  -.0061361   .1199083    -0.05   0.959      -.24124    .2289679
          i_rother |   .2731651   .1414716     1.93   0.054    -.0042181    .5505482
          i_female |  -.0291445   .0573755    -0.51   0.612    -.1416405    .0833515
     i_age_numeric |  -.0127762   .0018722    -6.82   0.000     -.016447   -.0091054
            i_educ |   .0305496   .0218335     1.40   0.162    -.0122594    .0733585
          i_income |   -.001173   .0003769    -3.11   0.002     -.001912   -.0004341
     i_logdistance |  -.0414787   .0136716    -3.03   0.002    -.0682846   -.0146728
          i_r_ideo |   .1205636   .0217388     5.55   0.000     .0779404    .1631868
      i_pid_folded |  -.0653225   .0270585    -2.41   0.016    -.1183759   -.0122691
          i_member |    .317958    .058426     5.44   0.000     .2034023    .4325137
        i_r_aidimp |   .2552002   .0326667     7.81   0.000     .1911506    .3192498
          i_r_know |   .1209868   .0375168     3.22   0.001     .0474276    .1945459
Conservative_Event |  -.9594966   .1589178    -6.04   0.000    -1.271086    -.647907
             Month |   .0124914   .0039165     3.19   0.001     .0048124    .0201705
             _cons |   -1.84441   .2228945    -8.27   0.000    -2.281439   -1.407381
------------------------------------------------------------------------------------

. 
. * Online Appendix A: Robustness check for Table 2
. 
. ttest i_rblack, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .0777026    .0054302    .2616665    .0670541    .0883512
       1 |     937    .1414846    .0111871    .3424415      .11953    .1634393
---------+--------------------------------------------------------------------
combined |   3,259    .0960407    .0050557     .288619     .086128    .1059534
---------+--------------------------------------------------------------------
    diff |            -.063782     .011116               -.0855771   -.0419869
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.7379
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_rlatino, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .0555797    .0046279    .2230052    .0465044    .0646549
       1 |     937    .0954102    .0094173    .2882675    .0769288    .1138917
---------+--------------------------------------------------------------------
combined |   3,259    .0670314    .0042774    .2441868    .0586447    .0754181
---------+--------------------------------------------------------------------
    diff |           -.0398306    .0094263               -.0583127   -.0213484
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.2255
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_rasian, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .0490218    .0043868    .2113857    .0404194    .0576242
       1 |     937    .0516474    .0070549    .2159545    .0378021    .0654927
---------+--------------------------------------------------------------------
combined |   3,259    .0497767    .0037255    .2126794    .0424722    .0570812
---------+--------------------------------------------------------------------
    diff |           -.0026256    .0082324               -.0187668    .0135157
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.3189
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3749         Pr(|T| > |t|) = 0.7498          Pr(T > t) = 0.6251

. ttest i_rother, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .0358096    .0037614    .1812522    .0284335    .0431857
       1 |     937     .047344     .006833    .2091616    .0339342    .0607538
---------+--------------------------------------------------------------------
combined |   3,259    .0391259    .0033236    .1897365    .0326093    .0456424
---------+--------------------------------------------------------------------
    diff |           -.0115344    .0073417               -.0259292    .0028603
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.5711
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0581         Pr(|T| > |t|) = 0.1163          Pr(T > t) = 0.9419

. ttest i_female, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .6119258    .0098739    .4757963    .5925632    .6312885
       1 |     937    .6873956    .0146921    .4497312    .6585624    .7162288
---------+--------------------------------------------------------------------
combined |   3,259    .6336243    .0082264    .4696266    .6174948    .6497537
---------+--------------------------------------------------------------------
    diff |           -.0754698    .0181304                -.111018   -.0399215
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.1626
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_age_numeric, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    44.30318    .3507623    16.90223    43.61534    44.99102
       1 |     937    39.08409    .5286226    16.18138    38.04667    40.12152
---------+--------------------------------------------------------------------
combined |   3,259    42.80263    .2953708    16.86202     42.2235    43.38177
---------+--------------------------------------------------------------------
    diff |             5.21909    .6462678                3.951958    6.486222
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   8.0757
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest i_educ, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    4.525429    .0295036    1.421694    4.467573    4.583285
       1 |     937    4.625403    .0425914    1.303742    4.541817    4.708988
---------+--------------------------------------------------------------------
combined |   3,259    4.554172    .0243371    1.389347    4.506455     4.60189
---------+--------------------------------------------------------------------
    diff |           -.0999738    .0537512               -.2053634    .0054158
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.8599
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0315         Pr(|T| > |t|) = 0.0630          Pr(T > t) = 0.9685

. ttest i_income, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    97.18302    1.885776    90.87016    93.48504     100.881
       1 |     937    77.59422     2.44051    74.70511    72.80471    82.38372
---------+--------------------------------------------------------------------
combined |   3,259    91.55102    1.523522    86.97432    88.56386    94.53818
---------+--------------------------------------------------------------------
    diff |            19.58881    3.349115                13.02222    26.15539
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   5.8490
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest i_logdistance, by(blm)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    4.517437    .0399818    1.926609    4.439033    4.595841
       1 |     937    3.941115    .0696369    2.131618    3.804453    4.077778
---------+--------------------------------------------------------------------
combined |   3,259    4.351738    .0351116    2.004436    4.282895    4.420581
---------+--------------------------------------------------------------------
    diff |            .5763215     .076929                .4254874    .7271557
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   7.4916
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest i_r_ideo, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    6.488755    .0476021     2.29381    6.395408    6.582102
       1 |     937    7.780149    .0449931     1.37726     7.69185    7.868448
---------+--------------------------------------------------------------------
combined |   3,259    6.860046    .0377123    2.152909    6.786103    6.933988
---------+--------------------------------------------------------------------
    diff |           -1.291394    .0802053               -1.448652   -1.134136
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -16.1011
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_pid_folded, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    2.031833    .0211708    1.020159    1.990317    2.073348
       1 |     937    2.085967    .0328368     1.00515    2.021524    2.150409
---------+--------------------------------------------------------------------
combined |   3,259    2.047397    .0177973    1.016008    2.012502    2.082292
---------+--------------------------------------------------------------------
    diff |            -.054134    .0393169               -.1312223    .0229543
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.3769
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0843         Pr(|T| > |t|) = 0.1686          Pr(T > t) = 0.9157

. ttest i_member, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    .4344689    .0101592    .4895445    .4145468     .454391
       1 |     937    .6038209    .0157754    .4828925    .5728616    .6347802
---------+--------------------------------------------------------------------
combined |   3,259    .4831595    .0086456    .4935572    .4662082    .5001109
---------+--------------------------------------------------------------------
    diff |            -.169352    .0188731               -.2063563   -.1323477
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -8.9732
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_r_aidimp, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    2.693701     .019761    .9522271     2.65495    2.732452
       1 |     937    3.185972    .0276307    .8457868    3.131747    3.240197
---------+--------------------------------------------------------------------
combined |   3,259    2.835235    .0166285    .9492798    2.802631    2.867838
---------+--------------------------------------------------------------------
    diff |           -.4922712    .0357186               -.5623043    -.422238
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t = -13.7819
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest i_r_know, by(blm) 

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,322    1.751521    .0148946    .7177261    1.722313    1.780729
       1 |     937    1.894344    .0232282    .7110243    1.848759    1.939929
---------+--------------------------------------------------------------------
combined |   3,259    1.792584    .0125879    .7186109    1.767903    1.817265
---------+--------------------------------------------------------------------
    diff |           -.1428227    .0277036               -.1971411   -.0885044
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -5.1554
Ho: diff = 0                                     degrees of freedom =     3257

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

.         
. probit blm i_rblack i_rlatino i_rasian i_rother i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ i_r_ideo i_pid_folded i_member i_r_aidimp i_r_know Conservative_Event Month

Iteration 0:   log likelihood = -1955.1017  
Iteration 1:   log likelihood = -1639.2051  
Iteration 2:   log likelihood = -1623.9966  
Iteration 3:   log likelihood = -1623.7686  
Iteration 4:   log likelihood = -1623.7685  

Probit regression                               Number of obs     =      3,259
                                                LR chi2(16)       =     662.67
                                                Prob > chi2       =     0.0000
Log likelihood = -1623.7685                     Pseudo R2         =     0.1695

------------------------------------------------------------------------------------
               blm |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
          i_rblack |   .4275877   .0823402     5.19   0.000     .2662039    .5889716
         i_rlatino |   .3376812    .100737     3.35   0.001     .1402402    .5351221
          i_rasian |   -.017256   .1170502    -0.15   0.883    -.2466703    .2121582
          i_rother |   .3431003    .137216     2.50   0.012     .0741619    .6120386
          i_female |   .0001322   .0563417     0.00   0.998    -.1102955    .1105598
     i_age_numeric |  -.0120906   .0018869    -6.41   0.000    -.0157889   -.0083923
            i_educ |   .0365723   .0218829     1.67   0.095    -.0063173    .0794619
          i_income |  -.0011428   .0003594    -3.18   0.001    -.0018472   -.0004385
     i_logdistance |  -.0425049   .0134165    -3.17   0.002    -.0688007   -.0162091
          i_r_ideo |   .1162053   .0193993     5.99   0.000     .0781834    .1542273
      i_pid_folded |    -.07314   .0267932    -2.73   0.006    -.1256536   -.0206263
          i_member |   .3374398   .0566456     5.96   0.000     .2264165    .4484631
        i_r_aidimp |   .2537384   .0312922     8.11   0.000     .1924068      .31507
          i_r_know |   .1152253     .03687     3.13   0.002     .0429614    .1874892
Conservative_Event |   -1.00883   .1462982    -6.90   0.000    -1.295569   -.7220908
             Month |   .0116271   .0039691     2.93   0.003     .0038477    .0194065
             _cons |  -1.858897   .2063924    -9.01   0.000    -2.263419   -1.454376
------------------------------------------------------------------------------------

. 
. * Table 3: Differences in Attitudes and Political Involvement 
. 
. impute blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_blm)
 15.57% (601) observations imputed

. replace j_blm = 0 if j_blm  < 0
(103 real changes made)

. replace j_blm  = 1 if j_blm  > 1
(0 real changes made)

. impute rblack blm rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_rblack)
 15.23% (588) observations imputed

. replace j_rblack = 0 if j_rblack < 0
(10 real changes made)

. replace j_rblack = 1 if j_rblack > 1
(0 real changes made)

. impute rlatino blm rblack rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_rlatino)
 15.23% (588) observations imputed

. replace j_rlatino = 0 if j_rlatino < 0
(2 real changes made)

. replace j_rlatino = 1 if j_rlatino > 1
(0 real changes made)

. impute rasian blm rblack rlatino rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_rasian)
 15.23% (588) observations imputed

. replace j_rasian = 0 if j_rasian < 0
(3 real changes made)

. replace j_rasian = 1 if j_rasian > 1
(0 real changes made)

. impute rother blm rblack rlatino rasian female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_rother)
 15.28% (590) observations imputed

. replace j_rother = 0 if j_rother < 0 
(0 real changes made)

. replace j_rother = 1 if j_rother > 1
(0 real changes made)

. impute female blm rblack rlatino rasian rother age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_female)
 16.09% (621) observations imputed

. replace j_female = 0 if j_female < 0
(0 real changes made)

. replace j_female = 1 if j_female > 1
(0 real changes made)

. impute age_numeric blm rblack rlatino rasian rother female educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_age_numeric)
 17.41% (672) observations imputed

. replace j_age_numeric = 0 if j_age_numeric < 0 
(0 real changes made)

. impute educ blm rblack rlatino rasian rother female age_numeric income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_educ)
 15.62% (603) observations imputed

. replace j_educ = 1 if j_educ < 1
(0 real changes made)

. replace j_educ = 6 if j_educ > 6
(0 real changes made)

. impute income blm rblack rlatino rasian rother female age_numeric educ logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_income)
 20.85% (805) observations imputed

. replace j_income = 0 if j_income < 0
(14 real changes made)

. impute logdistance blm rblack rlatino rasian rother female age_numeric educ income r_ideo pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_logdistance)
 19.69% (760) observations imputed

. replace j_logdistance = 0 if j_logdistance < 0
(0 real changes made)

. impute r_ideo blm rblack rlatino rasian rother female age_numeric educ income logdistance pid_folded member r_aidimp r_know Conservative_Event Month, gen(
> j_r_ideo)
 19.53% (754) observations imputed

. replace j_r_ideo = 1 if j_r_ideo < 1
(0 real changes made)

. replace j_r_ideo = 9 if j_r_ideo > 9
(0 real changes made)

. impute pid_folded blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo member r_aidimp r_know Conservative_Event Month, gen(
> j_pid_folded)
 20.60% (795) observations imputed

. replace j_pid_folded = 0 if j_pid_folded < 0 
(0 real changes made)

. replace j_pid_folded = 3 if j_pid_folded > 3 
(0 real changes made)

. impute member blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded r_aidimp r_know Conservative_Event Month, gen(
> j_member)
 10.98% (424) observations imputed

. replace j_member = 1 if j_member > 1 
(0 real changes made)

. replace j_member = 0 if j_member < 0 
(2 real changes made)

. impute r_aidimp blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_know Conservative_Event Month, gen(
> j_r_aidimp)
 13.89% (536) observations imputed

. replace j_r_aidimp = 1 if j_r_aidimp < 1
(0 real changes made)

. replace j_r_aidimp = 4 if j_r_aidimp > 4
(0 real changes made)

. impute r_know blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp Conservative_Event Month, gen(
> j_r_know)
 17.10% (660) observations imputed

. replace j_r_know = 1 if i_r_know < 1 
(0 real changes made)

. replace j_r_know = 3 if i_r_know > 3 
(574 real changes made)

. 
. svyset [pweight=weight], strata(RenumberedEvent)

      pweight: weight
          VCE: linearized
  Single unit: missing
     Strata 1: RenumberedEvent
         SU 1: <observations>
        FPC 1: <zero>

. 
. svy: oprobit r_intersectionality j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month          
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        13                  Number of obs     =      1,246
Number of PSUs     =     1,246                  Population size   = 1,261.2481
                                                Design df         =      1,233
                                                F(  17,   1217)   =      11.37
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------------
                    |             Linearized
r_intersectionality |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              j_blm |   .2937692   .0857679     3.43   0.001      .125502    .4620364
           j_rblack |   .2540997   .1652538     1.54   0.124      -.07011    .5783094
          j_rlatino |   .1574349   .1680911     0.94   0.349    -.1723413    .4872111
           j_rasian |  -.1817192   .1589745    -1.14   0.253    -.4936096    .1301712
           j_rother |  -.2036623   .2015969    -1.01   0.313    -.5991731    .1918486
           j_female |   .3018485   .0746782     4.04   0.000     .1553382    .4483588
      j_age_numeric |  -.0065375   .0023884    -2.74   0.006    -.0112234   -.0018517
             j_educ |  -.0139438   .0304946    -0.46   0.648    -.0737709    .0458833
           j_income |  -.0001697   .0003986    -0.43   0.670    -.0009518    .0006123
      j_logdistance |   .0221348   .0182645     1.21   0.226     -.013698    .0579677
           j_r_ideo |   .1523319   .0312945     4.87   0.000     .0909355    .2137282
       j_pid_folded |   .0306769   .0355436     0.86   0.388    -.0390557    .1004094
           j_member |   .0762942   .0791587     0.96   0.335    -.0790064    .2315947
         j_r_aidimp |   .2011363   .0415688     4.84   0.000     .1195829    .2826898
           j_r_know |  -.0984103   .0481302    -2.04   0.041    -.1928364   -.0039841
 Conservative_Event |  -.2507771   .1950575    -1.29   0.199    -.6334585    .1319042
              Month |  -.0162693   .0126904    -1.28   0.200    -.0411664    .0086277
--------------------+----------------------------------------------------------------
              /cut1 |  -.8124981   .3344331                     -1.468619   -.1563772
              /cut2 |  -.5289944   .3327932                     -1.181898    .1239092
              /cut3 |   .0927785    .330247                     -.5551296    .7406867
              /cut4 |   1.200871    .334386                      .5448424    1.856899
-------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      1,246
Model VCE    : Linearized

1._predict   : Pr(r_intersectionality==1), predict(pr outcome(1))
2._predict   : Pr(r_intersectionality==2), predict(pr outcome(2))
3._predict   : Pr(r_intersectionality==3), predict(pr outcome(3))
4._predict   : Pr(r_intersectionality==4), predict(pr outcome(4))
5._predict   : Pr(r_intersectionality==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0344316   .0053484     6.44   0.000     .0239386    .0449245
        1 2  |   .0194339   .0045839     4.24   0.000     .0104407    .0284271
        2 1  |   .0226696   .0044834     5.06   0.000     .0138737    .0314655
        2 2  |   .0143441   .0033884     4.23   0.000     .0076964    .0209919
        3 1  |     .09077   .0087369    10.39   0.000     .0736292    .1079107
        3 2  |    .063134    .008183     7.72   0.000     .0470799     .079188
        4 1  |   .3258143   .0145969    22.32   0.000     .2971769    .3544517
        4 2  |   .2741964   .0157503    17.41   0.000     .2432961    .3050968
        5 1  |   .5263145   .0163019    32.29   0.000     .4943321     .558297
        5 2  |   .6288916   .0240773    26.12   0.000     .5816545    .6761287
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Addressin
> g Intersectionality") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. predict r_inter_yhat, xb

. predict r_inter_blm if blm==1, xb
(2,923 missing values generated)

. predict r_inter_women if womensrts==1, xb
(2,248 missing values generated)

. predict r_inter_climate if climate==1, xb
(2,721 missing values generated)

. predict r_inter_aw if antiwar==1, xb 
(3,400 missing values generated)

. predict r_inter_teaparty if teaparty==1, xb
(3,714 missing values generated)

. mean r_inter_yhat

Mean estimation                   Number of obs   =      3,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
r_inter_yhat |    1.34826    .010122      1.328415    1.368105
--------------------------------------------------------------

. mean r_inter_blm 

Mean estimation                   Number of obs   =        937

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 r_inter_blm |   1.891929   .0130971      1.866225    1.917632
--------------------------------------------------------------

. mean r_inter_women

Mean estimation                   Number of obs   =      1,612

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
r_inter_women |   1.665711   .0114449      1.643262    1.688159
---------------------------------------------------------------

. mean r_inter_climate

Mean estimation                   Number of obs   =      1,139

-----------------------------------------------------------------
                |       Mean   Std. Err.     [95% Conf. Interval]
----------------+------------------------------------------------
r_inter_climate |   1.658599   .0138066       1.63151    1.685688
-----------------------------------------------------------------

. mean r_inter_aw

Mean estimation                   Number of obs   =        460

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_inter_aw |   1.663853    .022157      1.620311    1.707395
--------------------------------------------------------------

. mean r_inter_teaparty

Mean estimation                   Number of obs   =        146

------------------------------------------------------------------
                 |       Mean   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
r_inter_teaparty |   .3789301   .0368318      .3061336    .4517267
------------------------------------------------------------------

. sum r_inter_yhat

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_inter_yhat |      3,860     1.34826    .6288689  -.6677176   2.893985

. sum r_inter_blm 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 r_inter_blm |        937    1.891929    .4009086   .0286099   2.893985

. sum r_inter_women

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_inter_wo~n |      1,612    1.665711    .4595078   -.044116   2.893985

. sum r_inter_climate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_inter_cl~e |      1,139    1.658599    .4659606   -.157276   2.893985

. sum r_inter_aw

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  r_inter_aw |        460    1.663853    .4752148  -.1693837   2.643082

. sum r_inter_teaparty

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_inter_te~y |        146    .3789301    .4450403  -.5995497   1.980027

.         
. svy: oprobit socialmedia j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        24                  Number of obs     =      2,963
Number of PSUs     =     2,963                  Population size   = 3,095.7637
                                                Design df         =      2,939
                                                F(  17,   2923)   =      14.90
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------------
                   |             Linearized
       socialmedia |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .1955111   .0531153     3.68   0.000     .0913641    .2996582
          j_rblack |   .0898521   .0761276     1.18   0.238    -.0594166    .2391209
         j_rlatino |   .0052579    .091854     0.06   0.954    -.1748467    .1853626
          j_rasian |  -.0137071   .0973261    -0.14   0.888    -.2045414    .1771271
          j_rother |   -.133987    .128712    -1.04   0.298    -.3863617    .1183878
          j_female |   .0599286   .0466702     1.28   0.199     -.031581    .1514382
     j_age_numeric |   -.015437   .0015747    -9.80   0.000    -.0185247   -.0123493
            j_educ |   .0021473   .0181122     0.12   0.906    -.0333667    .0376612
          j_income |   .0002207   .0002899     0.76   0.447    -.0003478    .0007892
     j_logdistance |   .0092323   .0114907     0.80   0.422    -.0132983    .0317629
          j_r_ideo |   .0308297   .0151246     2.04   0.042     .0011739    .0604856
        pid_folded |   .0581456   .0208044     2.79   0.005     .0173529    .0989383
          j_member |    .235206   .0480418     4.90   0.000      .141007    .3294049
        j_r_aidimp |   .1286903   .0252359     5.10   0.000     .0792085    .1781721
          j_r_know |   .0268221   .0286676     0.94   0.350    -.0293886    .0830328
Conservative_Event |   .2479363   .0858744     2.89   0.004     .0795561    .4163164
             Month |  -.0022093    .003472    -0.64   0.525    -.0090172    .0045985
-------------------+----------------------------------------------------------------
             /cut1 |  -.4619097   .1671542                      -.789661   -.1341584
             /cut2 |  -.2772664   .1673757                     -.6054519    .0509192
             /cut3 |  -.1275756   .1681407                     -.4572611      .20211
             /cut4 |   .0253325   .1683626                      -.304788     .355453
             /cut5 |   .1350285   .1686002                     -.1955581    .4656151
             /cut6 |   .3057945   .1687755                     -.0251357    .6367246
             /cut7 |   .4444233   .1688685                      .1133108    .7755357
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      2,963
Model VCE    : Linearized

1._predict   : Pr(socialmedia==0), predict(pr outcome(0))
2._predict   : Pr(socialmedia==1), predict(pr outcome(1))
3._predict   : Pr(socialmedia==2), predict(pr outcome(2))
4._predict   : Pr(socialmedia==3), predict(pr outcome(3))
5._predict   : Pr(socialmedia==4), predict(pr outcome(4))
6._predict   : Pr(socialmedia==5), predict(pr outcome(5))
7._predict   : Pr(socialmedia==6), predict(pr outcome(6))
8._predict   : Pr(socialmedia==7), predict(pr outcome(7))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .2288996   .0085039    26.92   0.000     .2122254    .2455738
        1 2  |   .1765395   .0119104    14.82   0.000     .1531859    .1998931
        2 1  |   .0565841   .0043891    12.89   0.000     .0479782    .0651901
        2 2  |   .0492381   .0041165    11.96   0.000     .0411667    .0573096
        3 1  |   .0502854   .0041726    12.05   0.000     .0421038    .0584669
        3 2  |   .0450643   .0039124    11.52   0.000      .037393    .0527356
        4 1  |   .0546003    .004307    12.68   0.000     .0461552    .0630454
        4 2  |    .050259   .0041105    12.23   0.000     .0421991    .0583188
        5 1  |   .0406313   .0036913    11.01   0.000     .0333934    .0478691
        5 2  |   .0382781   .0035278    10.85   0.000     .0313609    .0451952
        6 1  |   .0645814   .0046048    14.02   0.000     .0555524    .0736103
        6 2  |   .0623751   .0044761    13.94   0.000     .0535985    .0711517
        7 1  |   .0526028   .0041095    12.80   0.000      .044545    .0606607
        7 2  |   .0522168   .0040867    12.78   0.000     .0442036      .06023
        8 1  |   .4518152   .0105523    42.82   0.000     .4311246    .4725059
        8 2  |   .5260292   .0173931    30.24   0.000     .4919253    .5601331
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Use of So
> cial Media") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. predict r_socmed_yhat, xb
(795 missing values generated)

. predict r_socmed_blm if blm==1, xb
(3,103 missing values generated)

. predict r_socmed_women if womensrts==1, xb
(2,455 missing values generated)

. predict r_socmed_climate if climate==1, xb
(2,905 missing values generated)

. predict r_socmed_aw if antiwar==1, xb 
(3,517 missing values generated)

. predict r_socmed_teaparty if teaparty==1, xb
(3,749 missing values generated)

. mean r_socmed_yhat

Mean estimation                   Number of obs   =      3,065

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
r_socmed_yhat |   .3683671   .0062173      .3561766    .3805575
---------------------------------------------------------------

. mean r_socmed_blm 

Mean estimation                   Number of obs   =        757

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
r_socmed_blm |   .6392977   .0109928      .6177177    .6608777
--------------------------------------------------------------

. mean r_socmed_women

Mean estimation                   Number of obs   =      1,405

----------------------------------------------------------------
               |       Mean   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
r_socmed_women |    .453963   .0090441      .4362215    .4717044
----------------------------------------------------------------

. mean r_socmed_climate

Mean estimation                   Number of obs   =        955

------------------------------------------------------------------
                 |       Mean   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
r_socmed_climate |   .4499296   .0114669      .4274264    .4724328
------------------------------------------------------------------

. mean r_socmed_aw

Mean estimation                   Number of obs   =        343

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 r_socmed_aw |   .4282041   .0175337      .3937167    .4626914
--------------------------------------------------------------

. mean r_socmed_teaparty

Mean estimation                   Number of obs   =        111

-------------------------------------------------------------------
                  |       Mean   Std. Err.     [95% Conf. Interval]
------------------+------------------------------------------------
r_socmed_teaparty |   .1856076   .0303652       .125431    .2457843
-------------------------------------------------------------------

. sum r_socmed_yhat

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_socmed_y~t |      3,065    .3683671    .3442025  -.8111238   1.361001

. sum r_socmed_blm 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_socmed_blm |        757    .6392977    .3024509  -.3343009   1.361001

. sum r_socmed_women

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_socmed_w~n |      1,405     .453963    .3390041  -.7091296   1.361001

. sum r_socmed_climate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_socmed_c~e |        955    .4499296     .354361  -.6248736   1.361001

. sum r_socmed_aw

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 r_socmed_aw |        343    .4282041    .3247278  -.3881257    1.32087

. sum r_socmed_teaparty

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_socmed_t~y |        111    .1856076    .3199169  -.4820825   .9133469

. 
. svy: oprobit r_demworks j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month  
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        24                  Number of obs     =      3,479
Number of PSUs     =     3,479                  Population size   = 3,623.7443
                                                Design df         =      3,455
                                                F(  17,   3439)   =      25.78
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------------
                   |             Linearized
        r_demworks |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |  -.2802239   .0490928    -5.71   0.000    -.3764776   -.1839701
          j_rblack |   .0579303   .0740901     0.78   0.434    -.0873346    .2031951
         j_rlatino |   .0977412   .0866216     1.13   0.259    -.0720935    .2675758
          j_rasian |   .0158909    .092492     0.17   0.864    -.1654535    .1972354
          j_rother |  -.1891065   .1018723    -1.86   0.063    -.3888425    .0106295
          j_female |   .0069185   .0429858     0.16   0.872    -.0773617    .0911987
     j_age_numeric |   .0029635   .0013925     2.13   0.033     .0002332    .0056937
            j_educ |   .0069756   .0164469     0.42   0.671     -.025271    .0392222
          j_income |   .0009954   .0002549     3.90   0.000     .0004956    .0014953
     j_logdistance |  -.0227279   .0105876    -2.15   0.032    -.0434864   -.0019694
          j_r_ideo |  -.0814847   .0155275    -5.25   0.000    -.1119287   -.0510406
      j_pid_folded |    .078223   .0203083     3.85   0.000     .0384055    .1180404
          j_member |  -.0579164   .0438787    -1.32   0.187    -.1439471    .0281143
        j_r_aidimp |  -.0922497   .0237208    -3.89   0.000     -.138758   -.0457414
          j_r_know |   .0328804   .0257852     1.28   0.202    -.0176754    .0834363
Conservative_Event |   .4618023   .0896799     5.15   0.000     .2859713    .6376333
             Month |  -.0150233   .0029935    -5.02   0.000    -.0208926    -.009154
-------------------+----------------------------------------------------------------
             /cut1 |   -1.54036   .1684091                     -1.870551   -1.210168
             /cut2 |  -.1788794   .1658196                     -.5039938    .1462349
             /cut3 |    1.22406   .1641718                      .9021763    1.545944
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      3,479
Model VCE    : Linearized

1._predict   : Pr(r_demworks==1), predict(pr outcome(1))
2._predict   : Pr(r_demworks==2), predict(pr outcome(2))
3._predict   : Pr(r_demworks==3), predict(pr outcome(3))
4._predict   : Pr(r_demworks==4), predict(pr outcome(4))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .1647628   .0071405    23.07   0.000     .1507628    .1787628
        1 2  |   .2387674   .0121895    19.59   0.000      .214868    .2626668
        2 1  |   .4542239   .0083567    54.35   0.000     .4378393    .4706085
        2 2  |   .4744751   .0088712    53.48   0.000     .4570818    .4918684
        3 1  |   .3255948    .008661    37.59   0.000     .3086135     .342576
        3 2  |   .2545792   .0113022    22.52   0.000     .2324195    .2767389
        4 1  |   .0554185   .0041362    13.40   0.000     .0473089    .0635281
        4 2  |   .0321783   .0039669     8.11   0.000     .0244007     .039956
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Satisfact
> ion with US Democracy") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. predict r_dw_yhat, xb

. predict r_dw_blm if blm==1, xb
(2,923 missing values generated)

. predict r_dw_women if womensrts==1, xb
(2,248 missing values generated)

. predict r_dw_climate if climate==1, xb
(2,721 missing values generated)

. predict r_dw_aw if antiwar==1, xb 
(3,400 missing values generated)

. predict r_dw_teaparty if teaparty==1, xb
(3,714 missing values generated)

. mean r_dw_yhat

Mean estimation                   Number of obs   =      3,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   r_dw_yhat |   -.565702   .0071556     -.5797311   -.5516729
--------------------------------------------------------------

. mean r_dw_blm 

Mean estimation                   Number of obs   =        937

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    r_dw_blm |  -.9992345   .0079814     -1.014898   -.9835709
--------------------------------------------------------------

. mean r_dw_women

Mean estimation                   Number of obs   =      1,612

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_dw_women |  -.7799257   .0077848     -.7951952   -.7646563
--------------------------------------------------------------

. mean r_dw_climate

Mean estimation                   Number of obs   =      1,139

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
r_dw_climate |   -.810591   .0094121      -.829058    -.792124
--------------------------------------------------------------

. mean r_dw_aw

Mean estimation                   Number of obs   =        460

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     r_dw_aw |  -.8357665   .0160741     -.8673544   -.8041786
--------------------------------------------------------------

. mean r_dw_teaparty

Mean estimation                   Number of obs   =        146

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
r_dw_teaparty |   .1481756   .0247213      .0993149    .1970363
---------------------------------------------------------------

. sum r_dw_yhat

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   r_dw_yhat |      3,860    -.565702    .4445684  -1.802685   .7947296

. sum r_dw_blm 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    r_dw_blm |        937   -.9992345    .2443147  -1.802685   .0182262

. sum r_dw_women

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  r_dw_women |      1,612   -.7799257    .3125589   -1.71333   .3835264

. sum r_dw_climate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_dw_climate |      1,139    -.810591    .3176499   -1.71333   .3835264

. sum r_dw_aw

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     r_dw_aw |        460   -.8357665    .3447501  -1.628475   .3432163

. sum r_dw_teaparty

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_dw_teapa~y |        146    .1481756    .2987088  -1.261251   .7653968

. 
. svy: oprobit violencejust j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month  
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        24                  Number of obs     =      3,375
Number of PSUs     =     3,375                  Population size   = 3,521.3972
                                                Design df         =      3,351
                                                F(  17,   3335)   =      22.98
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------------
                   |             Linearized
      violencejust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .4010243   .0540439     7.42   0.000     .2950619    .5069867
          j_rblack |   .0675292   .0772775     0.87   0.382    -.0839867    .2190451
         j_rlatino |   .1368026   .0990667     1.38   0.167    -.0574347    .3310398
          j_rasian |   .0539767    .104687     0.52   0.606    -.1512801    .2592336
          j_rother |   .2083329   .1395784     1.49   0.136    -.0653346    .4820003
          j_female |   -.362935   .0511351    -7.10   0.000    -.4631942   -.2626758
     j_age_numeric |  -.0135193   .0018598    -7.27   0.000    -.0171657   -.0098729
            j_educ |   .0002436   .0198897     0.01   0.990    -.0387536    .0392407
          j_income |  -.0008729   .0003698    -2.36   0.018    -.0015979   -.0001478
     j_logdistance |  -.0037363   .0124555    -0.30   0.764    -.0281575     .020685
          j_r_ideo |   .0791799   .0188497     4.20   0.000     .0422218     .116138
      j_pid_folded |  -.0152425   .0237371    -0.64   0.521    -.0617831    .0312982
          j_member |   .1862499   .0523421     3.56   0.000     .0836241    .2888756
        j_r_aidimp |   .1324003   .0291048     4.55   0.000     .0753354    .1894652
          j_r_know |  -.0187721   .0321743    -0.58   0.560    -.0818555    .0443112
Conservative_Event |  -.3043747   .1143626    -2.66   0.008    -.5286023   -.0801471
             Month |  -.0014054   .0036706    -0.38   0.702    -.0086022    .0057915
-------------------+----------------------------------------------------------------
             /cut1 |   .8495505   .1924968                      .4721273    1.226974
             /cut2 |    1.56755   .1929322                      1.189273    1.945827
             /cut3 |   2.299958   .1963646                      1.914952    2.684965
             /cut4 |   2.614002   .1987783                      2.224263    3.003741
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      3,375
Model VCE    : Linearized

1._predict   : Pr(violencejust==1), predict(pr outcome(1))
2._predict   : Pr(violencejust==2), predict(pr outcome(2))
3._predict   : Pr(violencejust==3), predict(pr outcome(3))
4._predict   : Pr(violencejust==4), predict(pr outcome(4))
5._predict   : Pr(violencejust==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .7809328   .0084289    92.65   0.000     .7644064    .7974591
        1 2  |   .6580133   .0148049    44.45   0.000     .6289858    .6870408
        2 1  |   .1437991   .0062498    23.01   0.000     .1315453    .1560529
        2 2  |   .1990675   .0091299    21.80   0.000     .1811668    .2169682
        3 1  |   .0581203   .0040806    14.24   0.000     .0501196     .066121
        3 2  |   .1023782   .0076109    13.45   0.000     .0874558    .1173006
        4 1  |   .0091412   .0014513     6.30   0.000     .0062956    .0119868
        4 2  |   .0196552   .0030824     6.38   0.000     .0136116    .0256987
        5 1  |   .0080066    .001435     5.58   0.000      .005193    .0108202
        5 2  |   .0208859   .0031416     6.65   0.000     .0147262    .0270456
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Violence 
> is Justified") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. predict r_vj_yhat, xb

. predict r_vj_blm if blm==1, xb
(2,923 missing values generated)

. predict r_vj_women if womensrts==1, xb
(2,248 missing values generated)

. predict r_vj_climate if climate==1, xb
(2,721 missing values generated)

. predict r_vj_aw if antiwar==1, xb 
(3,400 missing values generated)

. predict r_vj_teaparty if teaparty==1, xb
(3,714 missing values generated)

. mean r_vj_yhat 

Mean estimation                   Number of obs   =      3,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   r_vj_yhat |   .0958335   .0083957       .079373     .112294
--------------------------------------------------------------

. mean r_vj_blm 

Mean estimation                   Number of obs   =        937

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    r_vj_blm |     .64898   .0112577      .6268868    .6710731
--------------------------------------------------------------

. mean r_vj_women

Mean estimation                   Number of obs   =      1,612

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_vj_women |   .2983117    .011296      .2761553    .3204682
--------------------------------------------------------------

. mean r_vj_climate

Mean estimation                   Number of obs   =      1,139

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
r_vj_climate |   .3510341   .0136738      .3242053    .3778628
--------------------------------------------------------------

. mean r_vj_aw

Mean estimation                   Number of obs   =        460

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     r_vj_aw |   .3823244   .0209744      .3411067    .4235421
--------------------------------------------------------------

. mean r_vj_teaparty

Mean estimation                   Number of obs   =        146

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
r_vj_teaparty |  -.6437885   .0374591     -.7178248   -.5697521
---------------------------------------------------------------

. sum r_vj_yhat 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   r_vj_yhat |      3,860    .0958335    .5216183  -1.523872   1.660134

. sum r_vj_blm 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    r_vj_blm |        937      .64898    .3446023  -.3325999   1.660134

. sum r_vj_women

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  r_vj_women |      1,612    .2983117     .453532  -1.078359   1.616106

. sum r_vj_climate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_vj_climate |      1,139    .3510341    .4614791  -1.078359   1.616106

. sum r_vj_aw

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     r_vj_aw |        460    .3823244    .4498506  -.9314317   1.417961

. sum r_vj_teaparty

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_vj_teapa~y |        146   -.6437885      .45262  -1.440746   1.334739

.         
. svy: oprobit r_ang j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        24                  Number of obs     =      2,957
Number of PSUs     =     2,957                  Population size   = 3,083.7016
                                                Design df         =      2,933
                                                F(  17,   2917)   =      21.51
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------------
                   |             Linearized
             r_ang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .1395471   .0502716     2.78   0.006      .040976    .2381182
          j_rblack |  -.0464625   .0725178    -0.64   0.522    -.1886535    .0957284
         j_rlatino |  -.0847908   .0973977    -0.87   0.384    -.2757657     .106184
          j_rasian |  -.0715748   .0992545    -0.72   0.471    -.2661903    .1230406
          j_rother |  -.1033503   .1245407    -0.83   0.407    -.3475463    .1408457
          j_female |   .2244857   .0435489     5.15   0.000     .1390962    .3098753
     j_age_numeric |   .0030059   .0014797     2.03   0.042     .0001045    .0059073
            j_educ |  -.0034637   .0169779    -0.20   0.838    -.0367535    .0298262
          j_income |   -.000191   .0002713    -0.70   0.482    -.0007229    .0003409
     j_logdistance |  -.0057492   .0105929    -0.54   0.587    -.0265195    .0150211
          j_r_ideo |   .0794125   .0147893     5.37   0.000     .0504141     .108411
      j_pid_folded |   .0300687   .0218476     1.38   0.169    -.0127694    .0729069
          j_member |   .0508938   .0454049     1.12   0.262     -.038135    .1399226
        j_r_aidimp |   .1293083   .0239968     5.39   0.000     .0822561    .1763606
          j_r_know |  -.0427567   .0281619    -1.52   0.129    -.0979759    .0124625
Conservative_Event |  -.3712323   .0838664    -4.43   0.000    -.5356753   -.2067894
             Month |   .0005581    .003276     0.17   0.865    -.0058654    .0069816
-------------------+----------------------------------------------------------------
             /cut1 |  -.9733892   .1590841                     -1.285317   -.6614614
             /cut2 |  -.4176591   .1583498                      -.728147   -.1071711
             /cut3 |   .4871756   .1592189                      .1749835    .7993677
             /cut4 |   1.498202   .1606197                      1.183263    1.813141
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      2,957
Model VCE    : Linearized

1._predict   : Pr(r_ang==1), predict(pr outcome(1))
2._predict   : Pr(r_ang==2), predict(pr outcome(2))
3._predict   : Pr(r_ang==3), predict(pr outcome(3))
4._predict   : Pr(r_ang==4), predict(pr outcome(4))
5._predict   : Pr(r_ang==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0305375   .0032142     9.50   0.000     .0242351    .0368399
        1 2  |   .0227721   .0032428     7.02   0.000     .0164138    .0291304
        2 1  |   .0552639   .0043808    12.62   0.000     .0466742    .0638537
        2 2  |   .0447665   .0045844     9.76   0.000     .0357775    .0537556
        3 1  |   .2102608   .0079953    26.30   0.000     .1945838    .2259378
        3 2  |   .1855836   .0092535    20.06   0.000     .1674397    .2037276
        4 1  |   .3618727   .0090391    40.03   0.000     .3441492    .3795963
        4 2  |   .3555511   .0089735    39.62   0.000     .3379561    .3731461
        5 1  |    .342065   .0100715    33.96   0.000     .3223171     .361813
        5 2  |   .3913266   .0153061    25.57   0.000     .3613149    .4213384
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Anger") x
> title("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. predict r_ang_yhat, xb

. predict r_ang_blm if blm==1, xb
(2,923 missing values generated)

. predict r_ang_women if womensrts==1, xb
(2,248 missing values generated)

. predict r_ang_climate if climate==1, xb
(2,721 missing values generated)

. predict r_ang_aw if antiwar==1, xb 
(3,400 missing values generated)

. predict r_ang_teaparty if teaparty==1, xb
(3,714 missing values generated)

. mean r_ang_yhat

Mean estimation                   Number of obs   =      3,860

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_ang_yhat |   1.068445   .0067208      1.055268    1.081621
--------------------------------------------------------------

. mean r_ang_blm 

Mean estimation                   Number of obs   =        937

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   r_ang_blm |   1.377288    .008103      1.361385     1.39319
--------------------------------------------------------------

. mean r_ang_women

Mean estimation                   Number of obs   =      1,612

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 r_ang_women |   1.300123   .0066024      1.287173    1.313073
--------------------------------------------------------------

. mean r_ang_climate

Mean estimation                   Number of obs   =      1,139

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
r_ang_climate |   1.301669   .0078986      1.286172    1.317167
---------------------------------------------------------------

. mean r_ang_aw

Mean estimation                   Number of obs   =        460

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    r_ang_aw |   1.318843   .0134868       1.29234    1.345347
--------------------------------------------------------------

. mean r_ang_teaparty

Mean estimation                   Number of obs   =        146

----------------------------------------------------------------
               |       Mean   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
r_ang_teaparty |   .4398659   .0225015      .3953926    .4843392
----------------------------------------------------------------

. sum r_ang_yhat

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  r_ang_yhat |      3,860    1.068445    .4175579  -.1980262   1.858189

. sum r_ang_blm 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   r_ang_blm |        937    1.377288    .2480369   .1397989   1.858189

. sum r_ang_women

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 r_ang_women |      1,612    1.300123    .2650844   .0027957   1.858189

. sum r_ang_climate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_ang_clim~e |      1,139    1.301669    .2665714   .0027957   1.837665

. sum r_ang_aw

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    r_ang_aw |        460    1.318843    .2892602   .0229502   1.837665

. sum r_ang_teaparty

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_ang_teap~y |        146    .4398659    .2718866  -.1000882   1.417565

. 
. mean r_intersectionality

Mean estimation                   Number of obs   =      1,246

---------------------------------------------------------------------
                    |       Mean   Std. Err.     [95% Conf. Interval]
--------------------+------------------------------------------------
r_intersectionality |   4.358748   .0262956      4.307159    4.410337
---------------------------------------------------------------------

. mean socialmedia 

Mean estimation                   Number of obs   =      3,444

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 socialmedia |   4.465157   .0495243      4.368057    4.562257
--------------------------------------------------------------

. mean r_demworks

Mean estimation                   Number of obs   =      3,479

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_demworks |   2.210405   .0136242      2.183693    2.237118
--------------------------------------------------------------

. mean violencejust

Mean estimation                   Number of obs   =      3,375

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
violencejust |   1.400296   .0136709      1.373492      1.4271
--------------------------------------------------------------

. mean r_ang

Mean estimation                   Number of obs   =      2,957

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       r_ang |   3.970916   .0186966      3.934257    4.007576
--------------------------------------------------------------

. 
. svy: mean r_intersectionality
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      13        Number of obs   =      1,246
Number of PSUs   =   1,246        Population size = 1,261.2481
                                  Design df       =      1,233

---------------------------------------------------------------------
                    |             Linearized
                    |       Mean   Std. Err.     [95% Conf. Interval]
--------------------+------------------------------------------------
r_intersectionality |   4.341632   .0256832      4.291245     4.39202
---------------------------------------------------------------------

. svy: mean socialmedia 
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      3,444
Number of PSUs   =   3,444        Population size = 3,591.0984
                                  Design df       =      3,420

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 socialmedia |   4.463055    .049649      4.365711      4.5604
--------------------------------------------------------------

. svy: mean r_demworks
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      3,479
Number of PSUs   =   3,479        Population size = 3,623.7443
                                  Design df       =      3,455

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_demworks |   2.218508   .0130877      2.192847    2.244168
--------------------------------------------------------------

. svy: mean violencejust
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      3,375
Number of PSUs   =   3,375        Population size = 3,521.3972
                                  Design df       =      3,351

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
violencejust |   1.397309   .0131342      1.371557    1.423061
--------------------------------------------------------------

. svy: mean r_ang
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      24        Number of obs   =      2,957
Number of PSUs   =   2,957        Population size = 3,083.7016
                                  Design df       =      2,933

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       r_ang |   3.961629    .017909      3.926514    3.996745
--------------------------------------------------------------

. 
. sum r_intersectionality socialmedia r_demworks violencejust r_ang 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_intersec~y |      1,246    4.358748    .9282015          1          5
 socialmedia |      3,444    4.465157    2.906362          0          7
  r_demworks |      3,479    2.210405    .8035973          1          4
violencejust |      3,375    1.400296    .7942069          1          5
       r_ang |      2,957    3.970916    1.016691          1          5

. 
. * Online Appendix B: Robustness check for Table 3
. 
. oprobit r_intersectionality j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month          

Iteration 0:   log likelihood = -1328.5762  
Iteration 1:   log likelihood =  -1205.242  
Iteration 2:   log likelihood = -1204.7312  
Iteration 3:   log likelihood = -1204.7312  

Ordered probit regression                       Number of obs     =      1,246
                                                LR chi2(17)       =     247.69
                                                Prob > chi2       =     0.0000
Log likelihood = -1204.7312                     Pseudo R2         =     0.0932

-------------------------------------------------------------------------------------
r_intersectionality |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              j_blm |   .2658204   .0845523     3.14   0.002     .1001009    .4315398
           j_rblack |    .185257   .1459127     1.27   0.204    -.1007266    .4712406
          j_rlatino |   .2416608   .1602907     1.51   0.132    -.0725032    .5558247
           j_rasian |  -.1783409   .1483454    -1.20   0.229    -.4690926    .1124107
           j_rother |  -.1938606    .182036    -1.06   0.287    -.5506446    .1629233
           j_female |   .3131449   .0738891     4.24   0.000     .1683249    .4579649
      j_age_numeric |  -.0066468   .0023743    -2.80   0.005    -.0113004   -.0019932
             j_educ |  -.0145514   .0284817    -0.51   0.609    -.0703745    .0412716
           j_income |  -.0001209   .0004261    -0.28   0.777    -.0009562    .0007143
      j_logdistance |   .0233169   .0179247     1.30   0.193    -.0118149    .0584488
           j_r_ideo |    .165957   .0284088     5.84   0.000     .1102767    .2216372
       j_pid_folded |   .0275297   .0357632     0.77   0.441     -.042565    .0976243
           j_member |   .0744644   .0786205     0.95   0.344     -.079629    .2285577
         j_r_aidimp |   .1908224   .0408707     4.67   0.000     .1107174    .2709275
           j_r_know |   -.069967   .0482989    -1.45   0.147     -.164631    .0246971
 Conservative_Event |  -.1965518   .1735561    -1.13   0.257    -.5367155    .1436119
              Month |  -.0195104   .0125137    -1.56   0.119    -.0440368    .0050161
--------------------+----------------------------------------------------------------
              /cut1 |  -.7466335   .3238044                     -1.381278   -.1119885
              /cut2 |  -.4659125   .3216339                     -1.096303    .1644783
              /cut3 |   .1541162   .3198642                     -.4728061    .7810384
              /cut4 |   1.264987   .3208814                      .6360711    1.893903
-------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      1,246
Model VCE    : OIM

1._predict   : Pr(r_intersectionality==1), predict(pr outcome(1))
2._predict   : Pr(r_intersectionality==2), predict(pr outcome(2))
3._predict   : Pr(r_intersectionality==3), predict(pr outcome(3))
4._predict   : Pr(r_intersectionality==4), predict(pr outcome(4))
5._predict   : Pr(r_intersectionality==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0324207   .0050602     6.41   0.000     .0225029    .0423386
        1 2  |   .0192486   .0042766     4.50   0.000     .0108667    .0276305
        2 1  |   .0214222   .0042879     5.00   0.000     .0130181    .0298264
        2 2  |   .0140942    .003261     4.32   0.000     .0077028    .0204855
        3 1  |   .0872149    .008423    10.35   0.000      .070706    .1037237
        3 2  |   .0625093   .0080477     7.77   0.000     .0467362    .0782824
        4 1  |   .3221209   .0144546    22.29   0.000     .2937904    .3504513
        4 2  |   .2745883    .015792    17.39   0.000     .2436365    .3055401
        5 1  |   .5368213   .0164024    32.73   0.000     .5046731    .5689695
        5 2  |   .6295597   .0235448    26.74   0.000     .5834128    .6757066
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Addressin
> g Intersectionality") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

.         
. oprobit socialmedia j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month

Iteration 0:   log likelihood = -4752.9444  
Iteration 1:   log likelihood = -4627.2805  
Iteration 2:   log likelihood = -4627.1506  
Iteration 3:   log likelihood = -4627.1506  

Ordered probit regression                       Number of obs     =      2,963
                                                LR chi2(17)       =     251.59
                                                Prob > chi2       =     0.0000
Log likelihood = -4627.1506                     Pseudo R2         =     0.0265

------------------------------------------------------------------------------------
       socialmedia |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .1898319   .0534536     3.55   0.000     .0850649     .294599
          j_rblack |   .0485655   .0750108     0.65   0.517    -.0984531     .195584
         j_rlatino |    .040555   .0887954     0.46   0.648    -.1334807    .2145907
          j_rasian |   -.016041   .0972959    -0.16   0.869    -.2067374    .1746554
          j_rother |  -.1057194   .1225709    -0.86   0.388     -.345954    .1345152
          j_female |   .0553136   .0467504     1.18   0.237    -.0363155    .1469426
     j_age_numeric |  -.0155612    .001536   -10.13   0.000    -.0185718   -.0125506
            j_educ |   .0061857   .0178037     0.35   0.728    -.0287089    .0410802
          j_income |   .0002567    .000274     0.94   0.349    -.0002804    .0007937
     j_logdistance |   .0105362   .0115113     0.92   0.360    -.0120257     .033098
          j_r_ideo |   .0303853   .0149915     2.03   0.043     .0010025    .0597682
        pid_folded |   .0568244   .0202325     2.81   0.005     .0171695    .0964793
          j_member |   .2218188   .0471532     4.70   0.000     .1294001    .3142374
        j_r_aidimp |    .126522   .0245468     5.15   0.000     .0784112    .1746329
          j_r_know |   .0255565   .0284758     0.90   0.369    -.0302549     .081368
Conservative_Event |   .2448378   .0858542     2.85   0.004     .0765666     .413109
             Month |  -.0013471   .0034596    -0.39   0.697    -.0081277    .0054336
-------------------+----------------------------------------------------------------
             /cut1 |  -.4628953   .1670235                     -.7902553   -.1355353
             /cut2 |  -.2740476   .1667836                     -.6009375    .0528424
             /cut3 |  -.1260376    .166734                     -.4528303     .200755
             /cut4 |   .0268074   .1667752                     -.3000661    .3536808
             /cut5 |   .1383145   .1668202                     -.1886471    .4652762
             /cut6 |   .3084452   .1669068                     -.0186862    .6355766
             /cut7 |   .4483547   .1670023                      .1210362    .7756732
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      2,963
Model VCE    : OIM

1._predict   : Pr(socialmedia==0), predict(pr outcome(0))
2._predict   : Pr(socialmedia==1), predict(pr outcome(1))
3._predict   : Pr(socialmedia==2), predict(pr outcome(2))
4._predict   : Pr(socialmedia==3), predict(pr outcome(3))
5._predict   : Pr(socialmedia==4), predict(pr outcome(4))
6._predict   : Pr(socialmedia==5), predict(pr outcome(5))
7._predict   : Pr(socialmedia==6), predict(pr outcome(6))
8._predict   : Pr(socialmedia==7), predict(pr outcome(7))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .2267041   .0084385    26.87   0.000      .210165    .2432432
        1 2  |   .1759694   .0121601    14.47   0.000      .152136    .1998028
        2 1  |   .0577846   .0044393    13.02   0.000     .0490836    .0664855
        2 2  |    .050452   .0041719    12.09   0.000     .0422752    .0586287
        3 1  |    .049724   .0041049    12.11   0.000     .0416786    .0577694
        3 2  |   .0446866   .0038456    11.62   0.000     .0371493    .0522239
        4 1  |   .0545969   .0042553    12.83   0.000     .0462567    .0629372
        4 2  |   .0503557   .0040386    12.47   0.000     .0424403    .0582712
        5 1  |   .0413471   .0036973    11.18   0.000     .0341004    .0485937
        5 2  |   .0390127   .0035332    11.04   0.000     .0320878    .0459377
        6 1  |   .0644432   .0045259    14.24   0.000     .0555726    .0733138
        6 2  |   .0623047   .0044118    14.12   0.000     .0536577    .0709517
        7 1  |   .0531875   .0041069    12.95   0.000     .0451382    .0612369
        7 2  |   .0528167   .0040823    12.94   0.000     .0448155    .0608178
        8 1  |   .4522126   .0103777    43.58   0.000     .4318726    .4725525
        8 2  |   .5244022   .0174505    30.05   0.000     .4901999    .5586044
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Use of So
> cial Media") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

.                 
. oprobit r_demworks j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month  

Iteration 0:   log likelihood =  -4117.261  
Iteration 1:   log likelihood = -3853.3193  
Iteration 2:   log likelihood = -3852.7027  
Iteration 3:   log likelihood = -3852.7026  

Ordered probit regression                       Number of obs     =      3,479
                                                LR chi2(17)       =     529.12
                                                Prob > chi2       =     0.0000
Log likelihood = -3852.7026                     Pseudo R2         =     0.0643

------------------------------------------------------------------------------------
        r_demworks |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |  -.2903756   .0471805    -6.15   0.000    -.3828477   -.1979034
          j_rblack |   .0346418   .0674643     0.51   0.608    -.0975859    .1668695
         j_rlatino |   .0847504   .0797563     1.06   0.288    -.0715691    .2410699
          j_rasian |   .0359457   .0891091     0.40   0.687    -.1387051    .2105964
          j_rother |  -.1983392   .1030298    -1.93   0.054    -.4002738    .0035954
          j_female |   .0130202   .0414132     0.31   0.753    -.0681482    .0941886
     j_age_numeric |   .0028952   .0013701     2.11   0.035     .0002098    .0055805
            j_educ |   .0070291   .0159076     0.44   0.659    -.0241492    .0382073
          j_income |    .000991   .0002473     4.01   0.000     .0005062    .0014758
     j_logdistance |  -.0252675   .0102398    -2.47   0.014    -.0453372   -.0051979
          j_r_ideo |  -.0865951   .0132867    -6.52   0.000    -.1126366   -.0605537
      j_pid_folded |   .0776582   .0191715     4.05   0.000     .0400828    .1152337
          j_member |  -.0543687   .0416044    -1.31   0.191    -.1359119    .0271744
        j_r_aidimp |  -.0891829   .0220492    -4.04   0.000    -.1323985   -.0459672
          j_r_know |   .0286419   .0249713     1.15   0.251     -.020301    .0775847
Conservative_Event |   .4374557   .0761507     5.74   0.000      .288203    .5867084
             Month |  -.0145854   .0029988    -4.86   0.000     -.020463   -.0087078
-------------------+----------------------------------------------------------------
             /cut1 |  -1.594839   .1506843                     -1.890175   -1.299504
             /cut2 |  -.2249892   .1487683                     -.5165697    .0665913
             /cut3 |   1.178895   .1517254                      .8815183    1.476271
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      3,479
Model VCE    : OIM

1._predict   : Pr(r_demworks==1), predict(pr outcome(1))
2._predict   : Pr(r_demworks==2), predict(pr outcome(2))
3._predict   : Pr(r_demworks==3), predict(pr outcome(3))
4._predict   : Pr(r_demworks==4), predict(pr outcome(4))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .1652012   .0071864    22.99   0.000     .1511162    .1792862
        1 2  |   .2423811   .0117864    20.56   0.000     .2192803     .265482
        2 1  |   .4579949   .0083837    54.63   0.000     .4415632    .4744266
        2 2  |   .4779086   .0088334    54.10   0.000     .4605955    .4952218
        3 1  |   .3228945   .0086841    37.18   0.000     .3058739     .339915
        3 2  |   .2491669   .0109703    22.71   0.000     .2276656    .2706683
        4 1  |   .0539094   .0039144    13.77   0.000     .0462375    .0615814
        4 2  |   .0305433   .0035368     8.64   0.000     .0236114    .0374753
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Satisfact
> ion with US Democracy") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. 
. oprobit violencejust j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month  

Iteration 0:   log likelihood = -2775.8497  
Iteration 1:   log likelihood = -2538.1527  
Iteration 2:   log likelihood =  -2534.166  
Iteration 3:   log likelihood = -2534.1624  
Iteration 4:   log likelihood = -2534.1624  

Ordered probit regression                       Number of obs     =      3,375
                                                LR chi2(17)       =     483.37
                                                Prob > chi2       =     0.0000
Log likelihood = -2534.1624                     Pseudo R2         =     0.0871

------------------------------------------------------------------------------------
      violencejust |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .4091106   .0536471     7.63   0.000     .3039642    .5142569
          j_rblack |   .1211341   .0776104     1.56   0.119    -.0309794    .2732476
         j_rlatino |   .1234208   .0920421     1.34   0.180    -.0569784    .3038201
          j_rasian |   .0462895   .1059446     0.44   0.662    -.1613581    .2539371
          j_rother |   .2371395   .1226639     1.93   0.053    -.0032773    .4775562
          j_female |  -.3599868   .0510053    -7.06   0.000    -.4599555   -.2600182
     j_age_numeric |  -.0138818   .0017787    -7.80   0.000     -.017368   -.0103957
            j_educ |   .0106349   .0199107     0.53   0.593    -.0283894    .0496592
          j_income |  -.0008653   .0003401    -2.54   0.011     -.001532   -.0001987
     j_logdistance |  -.0013391   .0123085    -0.11   0.913    -.0254633    .0227851
          j_r_ideo |    .083815   .0170665     4.91   0.000     .0503653    .1172648
      j_pid_folded |  -.0153098   .0244024    -0.63   0.530    -.0631377     .032518
          j_member |   .1846162   .0517998     3.56   0.000     .0830905    .2861419
        j_r_aidimp |   .1266775   .0280443     4.52   0.000     .0717117    .1816433
          j_r_know |  -.0179526   .0319758    -0.56   0.574    -.0806241    .0447189
Conservative_Event |  -.2875392   .1060267    -2.71   0.007    -.4953477   -.0797306
             Month |  -.0010478   .0036226    -0.29   0.772    -.0081479    .0060523
-------------------+----------------------------------------------------------------
             /cut1 |   .9212664   .1854807                      .5577309    1.284802
             /cut2 |   1.635754   .1868187                      1.269596    2.001912
             /cut3 |   2.366124   .1912113                      1.991356    2.740891
             /cut4 |   2.683473   .1957179                      2.299873    3.067073
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      3,375
Model VCE    : OIM

1._predict   : Pr(violencejust==1), predict(pr outcome(1))
2._predict   : Pr(violencejust==2), predict(pr outcome(2))
3._predict   : Pr(violencejust==3), predict(pr outcome(3))
4._predict   : Pr(violencejust==4), predict(pr outcome(4))
5._predict   : Pr(violencejust==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .7808184   .0084781    92.10   0.000     .7642017    .7974351
        1 2  |   .6551147   .0146638    44.68   0.000     .6263743    .6838552
        2 1  |   .1434247   .0061593    23.29   0.000     .1313526    .1554968
        2 2  |    .199468   .0091909    21.70   0.000     .1814541    .2174819
        3 1  |   .0583897   .0041532    14.06   0.000     .0502496    .0665299
        3 2  |   .1037549    .007398    14.02   0.000     .0892552    .1182546
        4 1  |   .0093166   .0014948     6.23   0.000     .0063868    .0122464
        4 2  |   .0202897   .0031084     6.53   0.000     .0141973    .0263822
        5 1  |   .0080506   .0014069     5.72   0.000     .0052931    .0108081
        5 2  |   .0213727   .0032352     6.61   0.000     .0150318    .0277135
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Violence 
> is Justified") xtitle("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

.         
. oprobit r_ang j_blm j_rblack j_rlatino j_rasian j_rother j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Conservative_Event Month

Iteration 0:   log likelihood = -3886.6378  
Iteration 1:   log likelihood = -3686.9457  
Iteration 2:   log likelihood = -3686.8006  
Iteration 3:   log likelihood = -3686.8006  

Ordered probit regression                       Number of obs     =      2,957
                                                LR chi2(17)       =     399.67
                                                Prob > chi2       =     0.0000
Log likelihood = -3686.8006                     Pseudo R2         =     0.0514

------------------------------------------------------------------------------------
             r_ang |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             j_blm |   .1524953   .0498823     3.06   0.002     .0547278    .2502629
          j_rblack |  -.0799139   .0717605    -1.11   0.265    -.2205618    .0607341
         j_rlatino |  -.0819642   .0850324    -0.96   0.335    -.2486246    .0846963
          j_rasian |  -.0903963   .0933595    -0.97   0.333    -.2733776    .0925849
          j_rother |  -.1044337   .1109685    -0.94   0.347     -.321928    .1130607
          j_female |   .2215862    .042947     5.16   0.000     .1374117    .3057608
     j_age_numeric |   .0023553   .0014521     1.62   0.105    -.0004907    .0052013
            j_educ |  -.0004335    .016713    -0.03   0.979    -.0331904    .0323234
          j_income |  -.0001683   .0002568    -0.66   0.512    -.0006717    .0003351
     j_logdistance |  -.0064063   .0105861    -0.61   0.545    -.0271548    .0143421
          j_r_ideo |   .0811156   .0141377     5.74   0.000     .0534061    .1088251
      j_pid_folded |   .0335387     .02098     1.60   0.110    -.0075814    .0746587
          j_member |   .0432849   .0449786     0.96   0.336    -.0448716    .1314414
        j_r_aidimp |   .1328603   .0236277     5.62   0.000     .0865509    .1791697
          j_r_know |  -.0427338    .027639    -1.55   0.122    -.0969052    .0114376
Conservative_Event |  -.3581608   .0818169    -4.38   0.000     -.518519   -.1978027
             Month |   .0007647   .0032252     0.24   0.813    -.0055567     .007086
-------------------+----------------------------------------------------------------
             /cut1 |  -.9447953   .1618561                     -1.262027   -.6275632
             /cut2 |  -.3989912   .1581127                     -.7088864    -.089096
             /cut3 |   .4973089   .1576303                      .1883591    .8062586
             /cut4 |    1.50926   .1591724                      1.197288    1.821232
------------------------------------------------------------------------------------

. margins, at(j_blm=(0 1))

Predictive margins                              Number of obs     =      2,957
Model VCE    : OIM

1._predict   : Pr(r_ang==1), predict(pr outcome(1))
2._predict   : Pr(r_ang==2), predict(pr outcome(2))
3._predict   : Pr(r_ang==3), predict(pr outcome(3))
4._predict   : Pr(r_ang==4), predict(pr outcome(4))
5._predict   : Pr(r_ang==5), predict(pr outcome(5))

1._at        : j_blm           =           0

2._at        : j_blm           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0309418   .0032146     9.63   0.000     .0246413    .0372423
        1 2  |    .022448   .0031738     7.07   0.000     .0162275    .0286684
        2 1  |   .0544316   .0042822    12.71   0.000     .0460387    .0628244
        2 2  |   .0431947   .0043263     9.98   0.000     .0347153    .0516741
        3 1  |   .2073403   .0078962    26.26   0.000     .1918641    .2228165
        3 2  |   .1805545   .0091418    19.75   0.000     .1626368    .1984722
        4 1  |    .362321   .0089455    40.50   0.000      .344788    .3798539
        4 2  |     .35471    .008907    39.82   0.000     .3372527    .3721673
        5 1  |   .3449653   .0100006    34.49   0.000     .3253646    .3645661
        5 2  |   .3990928   .0152664    26.14   0.000     .3691712    .4290143
------------------------------------------------------------------------------

. marginsplot, title (" ") ytitle(, size(large)) xtitle(, size(large)) yscale(r (0 1)) ylabel (0 0.10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0) ytitle("Anger") x
> title("Not BLM Activist                              BLM Activist")

  Variables that uniquely identify margins: j_blm _outcome

. 
. oprobit r_intersectionality j_blm

Iteration 0:   log likelihood = -1328.5762  
Iteration 1:   log likelihood = -1303.5842  
Iteration 2:   log likelihood = -1303.5492  
Iteration 3:   log likelihood = -1303.5492  

Ordered probit regression                       Number of obs     =      1,246
                                                LR chi2(1)        =      50.05
                                                Prob > chi2       =     0.0000
Log likelihood = -1303.5492                     Pseudo R2         =     0.0188

-------------------------------------------------------------------------------------
r_intersectionality |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              j_blm |   .5240996   .0749188     7.00   0.000     .3772615    .6709377
--------------------+----------------------------------------------------------------
              /cut1 |  -1.760878   .0740768                     -1.906066    -1.61569
              /cut2 |  -1.527164   .0634607                     -1.651545   -1.402784
              /cut3 |  -.9957348    .049463                     -1.092681    -.898789
              /cut4 |  -.0016055   .0425164                     -.0849361    .0817251
-------------------------------------------------------------------------------------

. oprobit socialmedia j_blm 

Iteration 0:   log likelihood = -5512.1933  
Iteration 1:   log likelihood = -5475.5375  
Iteration 2:   log likelihood = -5475.5267  
Iteration 3:   log likelihood = -5475.5267  

Ordered probit regression                       Number of obs     =      3,444
                                                LR chi2(1)        =      73.33
                                                Prob > chi2       =     0.0000
Log likelihood = -5475.5267                     Pseudo R2         =     0.0067

------------------------------------------------------------------------------
 socialmedia |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       j_blm |   .3806276   .0446546     8.52   0.000     .2931062    .4681491
-------------+----------------------------------------------------------------
       /cut1 |  -.7139642   .0267305                      -.766355   -.6615733
       /cut2 |  -.5287654   .0258207                      -.579373   -.4781578
       /cut3 |  -.3960657   .0253679                      -.445786   -.3463455
       /cut4 |  -.2518539   .0250371                     -.3009256   -.2027821
       /cut5 |  -.1428587    .024885                     -.1916324   -.0940851
       /cut6 |   .0263666   .0248103                     -.0222608    .0749939
       /cut7 |   .1641982   .0248757                      .1154427    .2129536
------------------------------------------------------------------------------

. oprobit r_demworks j_blm 

Iteration 0:   log likelihood =  -4117.261  
Iteration 1:   log likelihood =  -4030.287  
Iteration 2:   log likelihood = -4030.2471  
Iteration 3:   log likelihood = -4030.2471  

Ordered probit regression                       Number of obs     =      3,479
                                                LR chi2(1)        =     174.03
                                                Prob > chi2       =     0.0000
Log likelihood = -4030.2471                     Pseudo R2         =     0.0211

------------------------------------------------------------------------------
  r_demworks |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       j_blm |   -.562599   .0428495   -13.13   0.000    -.6465825   -.4786154
-------------+----------------------------------------------------------------
       /cut1 |  -1.062552   .0287408                     -1.118883   -1.006221
       /cut2 |   .2344072   .0246008                      .1861905    .2826239
       /cut3 |   1.536768   .0374177                      1.463431    1.610106
------------------------------------------------------------------------------

. oprobit violencejust j_blm 

Iteration 0:   log likelihood = -2775.8497  
Iteration 1:   log likelihood = -2674.0363  
Iteration 2:   log likelihood = -2673.7856  
Iteration 3:   log likelihood = -2673.7856  

Ordered probit regression                       Number of obs     =      3,375
                                                LR chi2(1)        =     204.13
                                                Prob > chi2       =     0.0000
Log likelihood = -2673.7856                     Pseudo R2         =     0.0368

------------------------------------------------------------------------------
violencejust |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       j_blm |   .6922734   .0482587    14.35   0.000     .5976881    .7868588
-------------+----------------------------------------------------------------
       /cut1 |   .8671767   .0288983                      .8105371    .9238162
       /cut2 |   1.536467   .0356087                      1.466676    1.606259
       /cut3 |   2.224276   .0512527                      2.123823     2.32473
       /cut4 |   2.518882   .0634946                      2.394435    2.643329
------------------------------------------------------------------------------

. oprobit r_ang j_blm

Iteration 0:   log likelihood = -3886.6378  
Iteration 1:   log likelihood = -3850.8594  
Iteration 2:   log likelihood = -3850.8537  
Iteration 3:   log likelihood = -3850.8537  

Ordered probit regression                       Number of obs     =      2,957
                                                LR chi2(1)        =      71.57
                                                Prob > chi2       =     0.0000
Log likelihood = -3850.8537                     Pseudo R2         =     0.0092

------------------------------------------------------------------------------
       r_ang |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       j_blm |   .3841062   .0455679     8.43   0.000     .2947948    .4734175
-------------+----------------------------------------------------------------
       /cut1 |  -1.804557   .0477971                     -1.898238   -1.710877
       /cut2 |  -1.304663   .0351985                     -1.373651   -1.235675
       /cut3 |  -.4804401    .027357                     -.5340588   -.4268213
       /cut4 |   .4682061    .027317                      .4146657    .5217466
------------------------------------------------------------------------------

. 
. * Table 4, Model 1 See replication materials for the March for Racial Justice (MFRJ) data.
. 
. * Table 4, Model 2: How do Black-identified Black Lives Matter activists differ from allies to Black Lives Matter?
. 
. generate j_nonblack = 1 - j_rblack

. 
. svy: oprobit r_intersectionality j_nonblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        10                  Number of obs     =        394
Number of PSUs     =       394                  Population size   = 398.589859
                                                Design df         =        384
                                                F(  12,    373)   =       3.12
                                                Prob > F          =     0.0003

-------------------------------------------------------------------------------------
                    |             Linearized
r_intersectionality |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
         j_nonblack |  -.4726535   .2430475    -1.94   0.053    -.9505239     .005217
           j_female |   .2205238   .1528398     1.44   0.150    -.0799839    .5210315
      j_age_numeric |  -.0063281   .0043342    -1.46   0.145    -.0148497    .0021936
             j_educ |  -.0169805   .0553819    -0.31   0.759    -.1258703    .0919092
           j_income |  -.0002114   .0008071    -0.26   0.794    -.0017983    .0013755
      j_logdistance |  -.0054432   .0344202    -0.16   0.874    -.0731189    .0622325
           j_r_ideo |    .209763   .0631667     3.32   0.001      .085567     .333959
       j_pid_folded |   .0759188   .0642983     1.18   0.238     -.050502    .2023396
           j_member |   .1538448   .1487009     1.03   0.302    -.1385252    .4462148
         j_r_aidimp |   .1900677    .087303     2.18   0.030     .0184158    .3617195
           j_r_know |  -.0795179   .1042261    -0.76   0.446    -.2844433    .1254075
              Month |  -.0519988   .0240095    -2.17   0.031    -.0992054   -.0047923
--------------------+----------------------------------------------------------------
              /cut1 |  -1.546249   .5566262                     -2.640666   -.4518324
              /cut2 |  -1.304023   .5306209                     -2.347309   -.2607371
              /cut3 |  -.7748724   .5507743                     -1.857783    .3080385
              /cut4 |   .2937716   .5556321                     -.7986904    1.386234
-------------------------------------------------------------------------------------

. 
. svy: oprobit socialmedia j_nonblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        912
Number of PSUs     =       912                  Population size   = 945.765392
                                                Design df         =        895
                                                F(  12,    884)   =       4.40
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------
              |             Linearized
  socialmedia |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   j_nonblack |  -.0848758    .115387    -0.74   0.462    -.3113365    .1415849
     j_female |   .0120763   .0865938     0.14   0.889    -.1578743    .1820269
j_age_numeric |  -.0142967   .0027948    -5.12   0.000    -.0197818   -.0088116
       j_educ |   .0160128   .0334778     0.48   0.633    -.0496914    .0817169
     j_income |  -.0004181   .0006296    -0.66   0.507    -.0016537    .0008176
j_logdistance |   .0035025   .0202678     0.17   0.863    -.0362754    .0432804
     j_r_ideo |   .0516417   .0306959     1.68   0.093    -.0086027    .1118861
 j_pid_folded |   .0595729   .0421811     1.41   0.158    -.0232125    .1423584
     j_member |   .1264088   .0918482     1.38   0.169    -.0538541    .3066716
   j_r_aidimp |   .1071205   .0513742     2.09   0.037     .0062926    .2079483
     j_r_know |    .057173   .0561868     1.02   0.309    -.0531002    .1674463
        Month |  -.0066704   .0058427    -1.14   0.254    -.0181374    .0047966
--------------+----------------------------------------------------------------
        /cut1 |  -.7998366   .3067897                     -1.401948   -.1977257
        /cut2 |  -.5770229   .3061502                     -1.177879     .023833
        /cut3 |  -.4169384   .3087728                     -1.022942    .1890648
        /cut4 |  -.2318555   .3093666                     -.8390239    .3753129
        /cut5 |  -.1118214   .3109964                     -.7221886    .4985458
        /cut6 |   .0728072   .3114142                     -.5383798    .6839943
        /cut7 |   .2559814   .3115269                      -.355427    .8673897
-------------------------------------------------------------------------------

. 
. svy: oprobit r_demworks j_nonblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        912
Number of PSUs     =       912                  Population size   = 944.778409
                                                Design df         =        895
                                                F(  12,    884)   =       6.34
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------
              |             Linearized
   r_demworks |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   j_nonblack |  -.0727047   .1031814    -0.70   0.481    -.2752103     .129801
     j_female |   .1929593   .0872507     2.21   0.027     .0217196     .364199
j_age_numeric |   .0062649   .0028053     2.23   0.026     .0007591    .0117707
       j_educ |  -.0297507    .033182    -0.90   0.370    -.0948743    .0353729
     j_income |   .0012742   .0005703     2.23   0.026      .000155    .0023933
j_logdistance |  -.0349113   .0191468    -1.82   0.069    -.0724892    .0026665
     j_r_ideo |  -.1127121   .0338798    -3.33   0.001    -.1792051    -.046219
 j_pid_folded |   .1424309   .0396595     3.59   0.000     .0645945    .2202672
     j_member |  -.0569686   .0855398    -0.67   0.506    -.2248505    .1109133
   j_r_aidimp |   -.162004    .052958    -3.06   0.002    -.2659402   -.0580678
     j_r_know |  -.0655798   .0548918    -1.19   0.233    -.1733114    .0421517
        Month |  -.0200137   .0058348    -3.43   0.001    -.0314651   -.0085624
--------------+----------------------------------------------------------------
        /cut1 |  -1.835135    .315227                     -2.453805   -1.216465
        /cut2 |  -.4369039   .3082327                     -1.041847    .1680391
        /cut3 |   .7723481   .3021905                      .1792635    1.365433
-------------------------------------------------------------------------------

. 
. svy: oprobit violencejust j_nonblack i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        891
Number of PSUs     =       891                  Population size   = 924.967413
                                                Design df         =        874
                                                F(  12,    863)   =       7.94
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------
              |             Linearized
 violencejust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   j_nonblack |    .035534   .1176658     0.30   0.763    -.1954067    .2664746
     i_female |  -.4162789    .090876    -4.58   0.000    -.5946394   -.2379183
i_age_numeric |  -.0206278    .003458    -5.97   0.000    -.0274148   -.0138408
       i_educ |  -.0088066   .0356998    -0.25   0.805    -.0788739    .0612607
     i_income |  -.0005306   .0006817    -0.78   0.437    -.0018686    .0008074
i_logdistance |   .0059115   .0202956     0.29   0.771    -.0339224    .0457453
     j_r_ideo |   .1282182   .0362476     3.54   0.000     .0570757    .1993607
 j_pid_folded |  -.0795939   .0404919    -1.97   0.050    -.1590666   -.0001211
     j_member |   .2046353   .0893912     2.29   0.022     .0291888    .3800819
   j_r_aidimp |   .0948944   .0524938     1.81   0.071    -.0081342     .197923
     j_r_know |   .0308234   .0581434     0.53   0.596    -.0832936    .1449403
        Month |   .0094339   .0060093     1.57   0.117    -.0023604    .0212282
--------------+----------------------------------------------------------------
        /cut1 |   .4823232   .3547782                     -.2139936     1.17864
        /cut2 |   1.177275   .3557551                      .4790409    1.875509
        /cut3 |   1.947256   .3590938                       1.24247    2.652043
        /cut4 |   2.316456   .3651114                      1.599858    3.033053
-------------------------------------------------------------------------------

.         
. svy: oprobit r_ang j_nonblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        813
Number of PSUs     =       813                  Population size   =  842.62977
                                                Design df         =        796
                                                F(  12,    785)   =       4.63
                                                Prob > F          =     0.0000

-------------------------------------------------------------------------------
              |             Linearized
        r_ang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
   j_nonblack |   .1294376   .1184808     1.09   0.275    -.1031341    .3620093
     j_female |   .1944695   .0869075     2.24   0.026     .0238746    .3650644
j_age_numeric |   .0008588   .0030076     0.29   0.775    -.0050449    .0067625
       j_educ |  -.0290653   .0355654    -0.82   0.414    -.0988783    .0407477
     j_income |  -.0002054    .000535    -0.38   0.701    -.0012555    .0008448
j_logdistance |   .0340319   .0193854     1.76   0.080    -.0040207    .0720845
     j_r_ideo |   .1043951   .0347351     3.01   0.003     .0362119    .1725783
 j_pid_folded |   .0954643   .0421458     2.27   0.024     .0127343    .1781943
     j_member |   .0518783   .0905022     0.57   0.567    -.1257728    .2295294
   j_r_aidimp |   .1572911   .0508237     3.09   0.002     .0575269    .2570554
     j_r_know |   .0053454   .0573207     0.09   0.926    -.1071722     .117863
        Month |   .0091465   .0062423     1.47   0.143    -.0031069    .0213999
--------------+----------------------------------------------------------------
        /cut1 |  -.4384873    .346947                     -1.119526    .2425518
        /cut2 |   .1615307   .3448148                     -.5153232    .8383845
        /cut3 |   .9598796   .3490766                      .2746602    1.645099
        /cut4 |   2.033179   .3525336                      1.341174    2.725184
-------------------------------------------------------------------------------

.         
. svy: mean r_intersectionality if blm==1 & Conservative_Event==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      10        Number of obs   =        394
Number of PSUs   =     394        Population size = 398.589859
                                  Design df       =        384

---------------------------------------------------------------------
                    |             Linearized
                    |       Mean   Std. Err.     [95% Conf. Interval]
--------------------+------------------------------------------------
r_intersectionality |   4.610018   .0362082      4.538827    4.681209
---------------------------------------------------------------------

. svy: mean socialmedia if blm==1 & Conservative_Event==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      17        Number of obs   =        912
Number of PSUs   =     912        Population size = 945.765392
                                  Design df       =        895

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
 socialmedia |   5.158706   .0857244      4.990462     5.32695
--------------------------------------------------------------

. svy: mean r_demworks if blm==1 & Conservative_Event==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      17        Number of obs   =        912
Number of PSUs   =     912        Population size = 944.778409
                                  Design df       =        895

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  r_demworks |   1.928328   .0249196       1.87942    1.977235
--------------------------------------------------------------

. svy: mean violencejust if blm==1 & Conservative_Event==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      17        Number of obs   =        891
Number of PSUs   =     891        Population size = 924.967413
                                  Design df       =        874

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
violencejust |   1.733054   .0338691      1.666579    1.799528
--------------------------------------------------------------

. svy: mean r_ang if blm==1 & Conservative_Event==0
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =      17        Number of obs   =        813
Number of PSUs   =     813        Population size =  842.62977
                                  Design df       =        796

--------------------------------------------------------------
             |             Linearized
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       r_ang |   4.199371   .0319041      4.136745    4.261997
--------------------------------------------------------------

. sum r_intersectionality socialmedia r_demworks violencejust r_ang if blm==1 & Conservative_Event==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
r_intersec~y |        394    4.604061    .7348703          1          5
 socialmedia |        912     5.14693    2.590685          0          7
  r_demworks |        912    1.924342    .7625853          1          4
violencejust |        891    1.735129    1.027427          1          5
       r_ang |        813    4.214022     .899343          1          5

. 
. * Table 4, Model 3: Is there a cleavage withing BLM baased on activist background?
.         
. generate FE_AnyProBlack = FE_BLM + FE_ProBlackAntiRacism
(992 missing values generated)

. 
. impute FE_AnyProBlack blm rblack rlatino rasian rother female age_numeric educ income logdistance r_ideo pid_folded member r_aidimp r_know Conservative_Ev
> ent Month, gen(j_FE_AnyProBlack)
 25.70% (992) observations imputed

. replace j_FE_AnyProBlack = 0 if j_FE_AnyProBlack < 0
(35 real changes made)

. replace j_FE_AnyProBlack = 1 if j_FE_AnyProBlack > 1
(0 real changes made)

. 
. svy: oprobit r_intersectionality j_FE_AnyProBlack j_rblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        10                  Number of obs     =        394
Number of PSUs     =       394                  Population size   = 398.589859
                                                Design df         =        384
                                                F(  13,    372)   =       2.86
                                                Prob > F          =     0.0006

-------------------------------------------------------------------------------------
                    |             Linearized
r_intersectionality |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
   j_FE_AnyProBlack |   .1007099   .2151216     0.47   0.640    -.3222537    .5236736
           j_rblack |     .44158   .2443013     1.81   0.071    -.0387558    .9219157
           j_female |   .2191274   .1525905     1.44   0.152    -.0808902    .5191449
      j_age_numeric |  -.0062655   .0043019    -1.46   0.146    -.0147237    .0021927
             j_educ |  -.0171437   .0550856    -0.31   0.756     -.125451    .0911635
           j_income |  -.0002155   .0008032    -0.27   0.789    -.0017947    .0013636
      j_logdistance |  -.0071906   .0341816    -0.21   0.833    -.0743971    .0600159
           j_r_ideo |   .2092797   .0629626     3.32   0.001      .085485    .3330743
       j_pid_folded |   .0747322   .0643928     1.16   0.247    -.0518743    .2013388
           j_member |   .1601883   .1513992     1.06   0.291    -.1374869    .4578635
         j_r_aidimp |   .1894077   .0870055     2.18   0.030     .0183409    .3604746
           j_r_know |  -.0792002   .1040767    -0.76   0.447    -.2838316    .1254313
              Month |  -.0523571   .0240695    -2.18   0.030    -.0996816   -.0050326
--------------------+----------------------------------------------------------------
              /cut1 |  -1.072748   .5744219                     -2.202154    .0566576
              /cut2 |  -.8312037     .55156                     -1.915659    .2532521
              /cut3 |  -.3018489   .5663357                     -1.415356    .8116583
              /cut4 |   .7681425   .5728891                     -.3582496    1.894535
-------------------------------------------------------------------------------------

. 
. svy: oprobit socialmedia j_FE_AnyProBlack j_rblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        912
Number of PSUs     =       912                  Population size   = 945.765392
                                                Design df         =        895
                                                F(  13,    883)   =       4.13
                                                Prob > F          =     0.0000

----------------------------------------------------------------------------------
                 |             Linearized
     socialmedia |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
j_FE_AnyProBlack |  -.0853617   .1174727    -0.73   0.468    -.3159157    .1451922
        j_rblack |   .0992606   .1145241     0.87   0.386    -.1255063    .3240276
        j_female |   .0131568   .0867614     0.15   0.880    -.1571226    .1834363
   j_age_numeric |  -.0144138    .002796    -5.16   0.000    -.0199013   -.0089262
          j_educ |   .0156019   .0335649     0.46   0.642    -.0502732     .081477
        j_income |  -.0004479    .000632    -0.71   0.479    -.0016883    .0007924
   j_logdistance |   .0042972   .0203912     0.21   0.833    -.0357229    .0443174
        j_r_ideo |   .0501326    .030823     1.63   0.104    -.0103611    .1106263
    j_pid_folded |   .0594038   .0422286     1.41   0.160    -.0234748    .1422825
        j_member |   .1229779   .0917386     1.34   0.180    -.0570699    .3030257
      j_r_aidimp |    .106886   .0513541     2.08   0.038     .0060975    .2076746
        j_r_know |   .0577313   .0561794     1.03   0.304    -.0525274    .1679901
           Month |   -.006475   .0058519    -1.11   0.269    -.0179601      .00501
-----------------+----------------------------------------------------------------
           /cut1 |  -.7492408    .318318                     -1.373978   -.1245042
           /cut2 |  -.5262218   .3183486                     -1.151019     .098575
           /cut3 |   -.366042   .3209315                     -.9959081     .263824
           /cut4 |  -.1808582   .3213693                     -.8115833    .4498669
           /cut5 |  -.0607323   .3224957                     -.6936683    .5722037
           /cut6 |   .1239957   .3228517                     -.5096389    .7576303
           /cut7 |   .3071899   .3228943                     -.3265283    .9409082
----------------------------------------------------------------------------------

. 
. svy: oprobit r_demworks j_FE_AnyProBlack j_rblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        912
Number of PSUs     =       912                  Population size   = 944.778409
                                                Design df         =        895
                                                F(  13,    883)   =       5.96
                                                Prob > F          =     0.0000

----------------------------------------------------------------------------------
                 |             Linearized
      r_demworks |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
j_FE_AnyProBlack |  -.1668824   .1080508    -1.54   0.123    -.3789448      .04518
        j_rblack |   .0994578   .1024393     0.97   0.332    -.1015914    .3005071
        j_female |   .1946173   .0871746     2.23   0.026     .0235268    .3657078
   j_age_numeric |   .0059925   .0028024     2.14   0.033     .0004925    .0114925
          j_educ |  -.0296293   .0330325    -0.90   0.370    -.0944595    .0352009
        j_income |   .0012199   .0005707     2.14   0.033     .0000998      .00234
   j_logdistance |   -.033644   .0192348    -1.75   0.081    -.0713946    .0041065
        j_r_ideo |  -.1158082    .034037    -3.40   0.001    -.1826097   -.0490066
    j_pid_folded |   .1417002    .039712     3.57   0.000     .0637607    .2196398
        j_member |  -.0649083   .0855385    -0.76   0.448    -.2327877    .1029712
      j_r_aidimp |  -.1621144   .0528162    -3.07   0.002    -.2657725   -.0584563
        j_r_know |  -.0662364   .0549529    -1.21   0.228    -.1740879    .0416152
           Month |  -.0195868   .0058128    -3.37   0.001    -.0309952   -.0081785
-----------------+----------------------------------------------------------------
           /cut1 |   -1.83164   .3136616                     -2.447238   -1.216043
           /cut2 |  -.4318508   .3073554                     -1.035072    .1713703
           /cut3 |   .7800016    .304516                      .1823531     1.37765
----------------------------------------------------------------------------------

. 
. svy: oprobit violencejust j_FE_AnyProBlack j_rblack i_female i_age_numeric i_educ i_income i_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        891
Number of PSUs     =       891                  Population size   = 924.967413
                                                Design df         =        874
                                                F(  13,    862)   =       7.34
                                                Prob > F          =     0.0000

----------------------------------------------------------------------------------
                 |             Linearized
    violencejust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
j_FE_AnyProBlack |  -.1334954   .1273812    -1.05   0.295    -.3835041    .1165134
        j_rblack |  -.0149008   .1191776    -0.13   0.901    -.2488084    .2190069
        i_female |  -.4127868   .0904813    -4.56   0.000    -.5903728   -.2352007
   i_age_numeric |  -.0208778   .0034654    -6.02   0.000    -.0276794   -.0140763
          i_educ |  -.0093976   .0355649    -0.26   0.792    -.0792001    .0604049
        i_income |  -.0005903   .0006827    -0.86   0.388    -.0019303    .0007497
   i_logdistance |   .0063349   .0203333     0.31   0.755    -.0335728    .0462427
        j_r_ideo |    .126257   .0363525     3.47   0.001     .0549086    .1976054
    j_pid_folded |  -.0800816   .0404694    -1.98   0.048    -.1595102   -.0006531
        j_member |   .1981351   .0893372     2.22   0.027     .0227946    .3734756
      j_r_aidimp |   .0947768   .0524764     1.81   0.071    -.0082176    .1977713
        j_r_know |   .0295283   .0581276     0.51   0.612    -.0845577    .1436144
           Month |   .0095131   .0060059     1.58   0.114    -.0022746    .0213007
-----------------+----------------------------------------------------------------
           /cut1 |   .3866128   .3587715                     -.3175417    1.090767
           /cut2 |   1.082203   .3593232                      .3769661     1.78744
           /cut3 |   1.852354   .3617812                      1.142293    2.562416
           /cut4 |   2.221336   .3672267                      1.500587    2.942085
----------------------------------------------------------------------------------

.         
. svy: oprobit r_ang j_FE_AnyProBlack j_rblack j_female j_age_numeric j_educ j_income j_logdistance /*
>         */ j_r_ideo j_pid_folded j_member j_r_aidimp j_r_know Month if blm==1 & Conservative_Event==0
(running oprobit on estimation sample)

Survey: Ordered probit regression

Number of strata   =        17                  Number of obs     =        813
Number of PSUs     =       813                  Population size   =  842.62977
                                                Design df         =        796
                                                F(  13,    784)   =       4.26
                                                Prob > F          =     0.0000

----------------------------------------------------------------------------------
                 |             Linearized
           r_ang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
j_FE_AnyProBlack |  -.0202311   .1082418    -0.19   0.852    -.2327043    .1922421
        j_rblack |  -.1264308   .1192407    -1.06   0.289    -.3604942    .1076327
        j_female |   .1949349   .0868284     2.25   0.025     .0244951    .3653747
   j_age_numeric |   .0008293   .0030043     0.28   0.783     -.005068    .0067266
          j_educ |  -.0291246   .0355847    -0.82   0.413    -.0989756    .0407263
        j_income |  -.0002117   .0005369    -0.39   0.694    -.0012657    .0008424
   j_logdistance |    .034208   .0193971     1.76   0.078    -.0038676    .0722835
        j_r_ideo |   .1040017    .034778     2.99   0.003     .0357343     .172269
    j_pid_folded |   .0952286   .0421572     2.26   0.024     .0124762     .177981
        j_member |   .0509469   .0903904     0.56   0.573    -.1264849    .2283786
      j_r_aidimp |   .1570622   .0508532     3.09   0.002       .05724    .2568843
        j_r_know |   .0053694   .0573268     0.09   0.925    -.1071602    .1178989
           Month |   .0091865   .0062466     1.47   0.142    -.0030753    .0214484
-----------------+----------------------------------------------------------------
           /cut1 |  -.5772344   .3531555                      -1.27046    .1159917
           /cut2 |   .0226755   .3444959                     -.6535522    .6989033
           /cut3 |     .82101   .3467613                      .1403352    1.501685
           /cut4 |   1.894358   .3496216                      1.208069    2.580647
----------------------------------------------------------------------------------

. 
. * Multidimensional scaling
. 
. matrix A = (0,0.448949377,0.418335917,0.398314661,2.202530608\ /*
>         */ 0.448949377,0,0.039716133,0.082139134,1.753581231\ /*
>         */ 0.418335917,0.039716133,0,0.045602651,1.784194691\ /*
>         */ 0.398314661,0.082139134,0.045602651,0,1.804215946\ /*
>         */ 2.202530608,1.753581231,1.784194691,1.804215946,0)

.         
. matrix list A

symmetric A[5,5]
           c1         c2         c3         c4         c5
r1          0
r2  .44894938          0
r3  .41833592  .03971613          0
r4  .39831466  .08213913  .04560265          0
r5  2.2025306  1.7535812  1.7841947  1.8042159          0

. 
. mdsmat A
(row names of (dis)similarity matrix differ from column names; row names used)

Classical metric multidimensional scaling
    dissimilarity matrix: A

                                             Number of obs        =          5
    Eigenvalues > 0      =         3         Mardia fit measure 1 =     0.9996
    Retained dimensions  =         2         Mardia fit measure 2 =     1.0000

    --------------------------------------------------------------------------
                 |                   abs(eigenvalue)          (eigenvalue)^2
      Dimension  |  Eigenvalue      Percent    Cumul.       Percent    Cumul.
    -------------+------------------------------------------------------------
              1  |   2.9806086        99.89     99.89        100.00    100.00
              2  |   .00214828         0.07     99.96          0.00    100.00
    -------------+------------------------------------------------------------
              3  |   .00019355         0.01     99.97          0.00    100.00
    --------------------------------------------------------------------------

. 
. estat config

Configuration in 2-dimensional Euclidean space (principal normalization)

    Category |         dim1          dim2 
    ---------+----------------------------
          r1 |       0.6938        0.0019 
          r2 |       0.2446       -0.0304 
          r3 |       0.2753       -0.0070 
          r4 |       0.2952        0.0342 
          r5 |      -1.5088        0.0014 
    --------------------------------------

.         
. log close
      name:  <unnamed>
       log:  C:\Users\micha\OneDrive\Documents\Heaney_Code_2022-04-14.log
  log type:  text
 closed on:  14 Apr 2022, 22:34:18
------------------------------------------------------------------------------------------------------------------------------------------------------------
